Publications and Activities

Also see my Google Scholar, ORCiD and Harvard Catalyst profiles.

Journal and Conference Papers

“PRECISE Framework: Enhanced Radiology Reporting With GPT for Improved Readability, Reliability, and Patient-Centered Care”

S. Tripathi, L. Mutter, M. Muppuri, S. Dheer, E. Garza-Frias, K. Awan, A. Jha, M. Dezube, A. Tabari, B.C. Bizzo, K.J. Dreyer, C.P. Bridge*, and D. Daye*

European Journal of Radiology, April 2025

*equal contribution

[Science Direct]
“Using Interpretable Rule-Learning Artificial Intelligence to Optimally Differentiate Adrenal Pheochromocytomas from Adenomas with CT Radiomics”

D. Glazer, M. Viator, A. Sharp, J.B. Patel, B.E. Dabiri, C.P. Bridge, J.A. Barletta, O. Pianykh, and W.M. Mayo-Smith

Abdominal Radiology, Mar 2025

[Springer Link]
“A Review of Deep Learning for Brain Tumor Analysis in MRI”

F.J. Dorfner, J. Patel, J. Kalpathy-Cramer, E.R. Gerstner, and C.P. Bridge

npj Precision Oncology, Jan 2025

[Nature]
“Investigating the Potential of Diffusion Tensor Atlases to Generate Anisotropic Clinical Tumor Volumes in Glioblastoma Patients”

K. Hochreuter, G. Buti, A. Ajdari, C.P. Bridge, G.C. Sharp, S. Jespersen, S. Lukacova, T. Bortfeld, and J.F. Kallehauge

Physics and Imaging in Radiation Oncology 33, Jan 2025

[ScienceDirect]
“Large Language Models as an Academic Resource for Radiologists Stepping into Artificial Intelligence Research”

S. Tripathi, J. Patel, L. Mutter, F.J. Dorfner, C.P. Bridge, and D. Daye

Current Problems in Diagnostic Radiology, Dec 2024

[ScienceDirect]
“Comparing Commercial and Open-Source Large Language Models for Labeling Chest Radiograph Reports”

F.J. Dorfner, L. Jürgensen, L. Donle, F. Al Mohamad, T.R. Bodenmann, M.C. Cleveland, F. Busch, L.C. Adams, J. Sato, T. Schultz, A.E. Kim, J. Merkow, K.K. Bressem*, and C.P. Bridge*

Radiology, Oct 2024

*equal contribution

[RSNA]
“Addressing Catastrophic Forgetting for Medical Domain Expansion”

S. Gupta, K. Chang, L. Qu, M. Aggarwal, N. Arun, A. Vaswani, S. Raghavan, V. Agarwal, M. Gidwani, K. Hoebel, J. Patel, C. Lu, C.P. Bridge, D.L. Rubin, P. Singh*, and J. Kalpathy-Cramer*

International Workshop on Personalized Incremental Learning in Medicine, Medical Image Computing and Computer-Aided Intervention 2024

*equal contribution

[Springer] [arXiv]
“Beyond MELD Score: Association of Machine Learning-derived CT Body Composition with 90-Day Mortality Post Transjugular Intrahepatic Portosystemic Shunt Placement”

T.S. Elhakim, A. Mansur, J. Kondo, O.M.F. Omar, K. Ahmed, A. Tabari, A. Brea, G. Ndakwah, S. Iqbal, A.S. Allegretti, F.J. Fintelmann, E. Wehrenberg-Klee, C.P. Bridge, and D. Daye

Cardiovascular and Interventional Radiology, 2024

[Springer]
“Scalable Drift Monitoring in Medical Imaging AI”

J. Merkow*, F.J. Dorfner*, X. Yang, A. Ersoy, G. Dasegowda, M. Kalra, M.P. Lungren, C.P. Bridge*, and I. Tarapov*

Preprint

*equal contribution

[arXiv]
“Multimodal Deep Learning-Based Prediction of Immune Checkpoint Inhibitor Efficacy in Brain Metastases”

T.R. Bodenmann, N. Gil, F.J. Dorfner, M.C. Cleveland, J.B. Patel, S.B. Brahmavar, M.S. Gülen, D. Pulido-Arias, J. Kalpathy-Cramer, J.-P. Thiran, E. Gerster, A.E. Kim*, and C.P. Bridge*

Cancer Prevention, Detection, and Intervention. CaPTion 2024.

*equal contribution
[Springer]
“No Code Machine Learning: Validating the Approach on Use-Case for Classifying Clavicle Fractures”

G. Dasegowda, J.Y. Sato, D.C. Elton, E. Garza-Frias, T. Schultz, C.P. Bridge, B.C. Bizzo, M.K. Kalra, and K.J. Dreyer

Clinical Imaging, 110207, 2024

[ScienceDirect]
“Diffusion Tensor Transformation for Personalizing Target Volumes in Radiation Therapy”

G. Buti, A. Ajdari, Y.L. Chen, C.P. Bridge, G.C. Sharp, and T. Bortfeld

Medical Image Analysis 97, 103271, 2024

[ScienceDirect]
“Integrating Muscle Fiber Orientation from Visible Human Data into Radiotherapy Target Volumes”

G. Buti, A. Ajdari, Y.L. Chen, C.P. Bridge, G.C. Sharp, and T. Bortfeld

Physics in Medicine and Biology 69 (14), 2024

[IOPScience]
“CASCADE: Context-Aware Data-Driven AI for Streamlined Multidisciplinary Tumor Board Recommendations in Oncology”

D. Daye*, R. Parker*, S. Tripathi, M. Cox, S.B. Orama, L. Valentin, C.P. Bridge, and R.N. Uppot

Cancers 16(11) 2024

*equal contribution

[MDPI]
“Deep Learning-based Prediction of Breast Cancer Tumor and Immune Phenotypes from Histopathology”

T. Gonçalves, D. Pulido-Arias, J. Willett, K.V. Hoebel, M. Cleveland, S.R. Ahmed, E. Gerstner, J. Kalpathy-Cramer, J.S. Cardoso, C.P. Bridge*, and A.E. Kim*

Imageomics Workshop (Imageomics-AAAI-24), 38th Annual AAAI Conference on Artificial Intelligence

*equal contribution

[arXiv]
“The Influence of Anisotropy on the Clinical Target Volume of Brain Tumor Patients”

G. Buti, A. Ajdari, K. Hochreuter, H. Shih, C.P. Bridge, G.C. Sharp, and T. Bortfeld

Physics in Medicine and Biology, 69(3), 2024

[IOPScience]
“From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer”

S. Tripathi, A. Tabari, A. Mansur, H. Dabbara, C.P. Bridge, and D. Daye

Diagnostics 14 (2), 2024

[MDPI]
“Enrichment of the NLST and NSCLC-Radiomics Computed Tomography Collections with AI-derived Annotations”

D. Krishnaswamy, V. Thiriveedhi, D. Punzo, D. Clunie, C.P. Bridge, H.J.W.L. Aerts, R. Kikinis, and A. Fedorov

Scientific Data 11, 2024

[Nature] [arXiv]
“Conformal Triage for Medical Imaging AI Deployment”

A.N. Angelopoulos, S. Pomerantz, S. Do, S. Bates, C.P. Bridge, D.C. Elton, M.H. Lev, R.G Gonzalez, M.I. Jordan, and J. Malik

preprint, 2024

[medRxiv]
“A Deep Learning Based Framework for Joint Image Registration and Segmentation of Brain Metastases on Magnetic Resonance Imaging”

J. Patel, S.R. Ahmed, K. Chang, P. Singh, M. Gidwani, K. Hoebel, A. Kim, C.P. Bridge, C.-J. Teng, X. Li, G. Xu, M. McDonald, A. Aizer, W.L. Bi, I. Ly, B. Rosen, P. Brastianos, R. Huang, E. Gerstner, and J. Kalpathy-Cramer

Machine Learning for Healthcare, 2023

[Proceedings of Machine Learning Research]
“Abnormal Vascular Structure and Function Within Brain Metastases is Linked to Pembrolizumab Resistance”

A.E. Kim, K.W. Lou, A. Giobbie-Hurder, K. Chang, M. Gidwani, K. Hoebel, J.B. Patel, M.C. Cleveland, P. Singh, C.P. Bridge, S.R. Ahmed, B.A. Bearce, W. Liu, E. Fuster-Garcia, E.Q. Lee, N.U. Lin, B. Overmoyer, P. Y Wen, L. Nayak, J.V Cohen, J. Dietrich, A. Eichler, R. Heist, I. Krop, D. Lawrence, J. Ligibel, S. Tolaney, E. Mayer, E. Winer, C.M. Perrino, E.J. Summers, M. Mahar, K. Oh, H.A. Shih, D.P. Cahill, B. R. Rosen, Y.-F. Yen, J. Kalpathy-Cramer, M. Martinez-Lage, R.J. Sullivan, P.K. Brastianos, K.E. Emblem, and E.R. Gerstner

Neuro-Oncology, 2023

[Oxford University Press]
“Machine Learning–Based Detection of Sarcopenic Obesity and Association with Adverse Outcomes in Patients Undergoing Surgical Treatment for Spinal Metastases”

S.I. Khalid, E. Massaad, A. Kiapour, C.P. Bridge, G. Rigney, A. Burrows, J. Shim ,R. De la Garza Ramos, D.G. Tobert, A.J. Schoenfeld, T. Williamson, G.M. Shankar, and J.H. Shin

Journal Of Neurosurgery, Dec 2023

[Journal of Neurosurgery]
“Not without Context—A Multiple Methods Study on Evaluation and Correction of Automated Brain Tumor Segmentations by Experts”

K.V. Hoebel, C.P. Bridge, A. Kim, E.R. Gerstner, I.K. Ly, F. Deng, M.N. DeSalvo, J. Diettrich, R. Huang, S.Y. Huang, S.R. Pomerantz, S. Vagvala, B.R. Rosen, and J. Kalpathy-Cramer

Academic Radiology, Nov 2023

[Science Direct]
“Point-of-care AI-Assisted Stepwise Ultrasound Pneumothorax Diagnosis”

K. Kim, F. Macruz, D. Wu, C.P. Bridge, S.E. McKinney, A. Alhassan, E. Sharaf, A. Pely, P. Danset, T. Duffy, D. Dhatt, V. Buch, A. Liteplo, and Q. Li

Physics in Medicine and Biology, Volume 68 Number 20

[IOP Science]
“NCI Imaging Data Commons: Towards Transparency, Reproducibility, and Scalability in Imaging AI”

A. Fedorov, W.J. Longabaugh, D. Pot, D.A. Clunie, S.D. Pieper, D.L. Gibbs, C.P. Bridge, M. D. Herrmann, A. Homeyer, R. Lewis, H.J.W.L. Aerts, D. Krishnaswamy, V.K. Thiriveedhi, C. Ciausu, D. Schacherer, D. Bontempi, T. Pihl, U. Wagner, K. Farahani, E. Kim, and R. Kikinis

Radiographics, November 2023

[RSNA]
“Expert-Centered Evaluation of Deep Learning Algorithms for Brain Tumor Segmentation”

K.V. Hoebel, C.P. Bridge, S.M.E.H. Ahmed, A. Oluwatosin, C. Chung, R.Y Huang, J.M. Johnson, A.E. Kim, I. Ly, K. Chang, J.B. Patel, M. Pinho, T. Batchelor, B.R. Rosen, E.R. Gerstner, and J. Kalpathy-Cramer

Radiology: Artificial Intelligence, Nov 2023

[RSNA]
“CheXstray: A Real-Time Multi-Modal Monitoring Workflow for Medical Imaging AI”

J. Merkow, A. Soin, J. Long, J.P. Cohen, S. Saligrama, C.P. Bridge, X. Yang, S. Kaiser, S. Borg, I. Tarapov, and M.P. Lungren

MICCAI 2023, (oral presentation)

[Springer]
“Body Composition and Lung Cancer-Associated Cachexia in TRACERx”

O. Al-Sawaf, J. Weiss, M. Skrzypski J.M. Lam, T. Karasaki, F. Zambrana, A.C. Kidd, A.M. Frankell, T.B.K. Watkins, C. Martinez-Ruiz, C. Puttick, J..R.M. Black, A. Huebner, M. Al Bakir, M. Sokac, S. Collins, S. Veeriah, N. Magno, C. Naceur-Lombardelli, P. Prymas, A. Toncheva, S. Ward, N. Jayanth, R. Salgado, C.P. Bridge, D.C. Christiani, R.H. Mak, C. Bay, M. Rosenthal, N. Sattar, P. Welsh, Y. Liu, N. Perrimon, K. Popuri, M.F. Beg, N. McGranahan, A. Hackshaw, D.M. Breen, S. O'Rahilly, N.J. Birkbak, H.J.W.L. Aerts, TRACERx Consortium, M. Jamal-Hanjani and C. Swanton

Nature Medicine, April 2023

[Nature]
“Addressing the Challenges of Implementing Artificial Intelligence Tools in Clinical Practice: Principles From Experience”

B.C. Bizzo, G. Dasegowda, C.P. Bridge, B. Miller, J.M. Hillis, M.K. Kalra, K. Durniak, M. Stout, T. Schultz, T. Alkasab, and K.J. Dreyer

Journal of the American College of Radiology, 20(3), March 2023, pp 352-360

[ScienceDirect]
“Role of Machine Learning-Based CT Body Composition in Risk Prediction and Prognostication: Current State and Future Directions”

T. Elhakim, K. Trinh, A. Mansur, C.P. Bridge, and D. Daye

MDPI Diagnostics 2023, 13(5)

[MDPI]
“Enhanced Physician Performance When Using an Artificial Intelligence Model to Detect Ischemic Stroke on Computed Tomography”

J.M. Hillis, B.C. Bizzo, R. Gauriau, C.P. Bridge, J.K. Chin, B. Hakamy, S. Mercaldo, J. Conklin, S. Dutta, W.A. Mehan, R.W. Regenhardt, A. Singh, A.B. Singhal, J.D. Sonis, M.D. Succi, T. Zhang, B. Xing, J.F. Kalafut, K.J. Dreyer, M.H. Lev, and R.G. Gonzalez

Preprint

[medRxiv]
“Head CT Deep Learning Model is Highly Accurate for Early Infarct Estimation”

R. Gauriau, B.C. Bizzo, D.S. Comeau, J.M. Hillis, C.P. Bridge, J.K. Chin, J. Pawar, A. Pourvaziri, I. Sesic, E. Sharaf, J. Cao, F.T.C. Noro, W.F. Wiggins, M.T. Caton, F. Kitamura, K.J. Dreyer, J.F. Kalafut, K.P. Andriole, S.R. Pomerantz, R.G. Gonzalez, and M.H. Lev

Scientific Reports, 5;13(1):189 (2023)

[Nature]
“Improving Repeatability of Deep Learning Models with Monte Carlo Dropout”

A. Lemay, K. Hoebel, C.P. Bridge, B. Befano, S. De Sanjosé, D. Egemen, A. Rodriguez, M. Schiffman, J.P. Campbell, and J. Kalpathy-Cramer

npj Digital Medicine 5 174, 2022

[Nature] [Blog]
“Machine Learning for Adrenal Gland Segmentation and Adrenal Masses on CT”

C. Weiss, J. Patel, B. Bizzo, D. Glazer, C.P. Bridge, K. Andriole, B. Dabiri, J. Chin, John, K. Dreyer, J. Kalpathy-Cramer, and W. Mayo-Smith

Radiology, Sep 2022

[Radiology]
“Utility of Normalized Body Composition Areas, Derived From Outpatient Abdominal CT Using a Fully Automated Deep Learning Method, for Predicting Subsequent Cardiovascular Events”

K. Magudia, C.P. Bridge, C.P. Bay, S. Farah, A. Babic, F.J. Fintelmann, L.K. Brais, K.P. Andriole, B.M. Wolpin, and M.H. Rosenthal

American Journal of Roentgenology, August 2022

[AJR]
“Highdicom: A Python Library for Standardized Encoding of Image Annotations and Machine Learning Model Outputs in Pathology and Radiology”

C.P. Bridge, C. Gorman, S. Pieper, S.W. Doyle, J.K. Lennerz, J. Kalpathy-Cramer, D.A. Clunie, A.Y. Fedorov, and M.D. Herrmann

Journal of Digital Imaging, August 2022

JDI Editor's Choice Article 2023

[Springer] [arXiv]
“Evaluating Frailty, Mortality, and Complications Associated with Metastatic Spine Tumor Surgery Using Machine Learning-Derived Body Composition Analysis”

E. Massaad, C.P. Bridge, A. Kiapour, M.S. Fourman, J.B. Duvall, I.D. Connolly, M. Hadzipasic, G.M. Shankar, K.P. Andriole, M. Rosenthal, A.J. Schoenfeld, M.H. Bilsky, and J.H. Shin

Neurosurgery, February 2022

[Neurosurgery]
“Development and Clinical Application of a Deep Learning Model to Identify Acute Infarct on Magnetic Resonance Imaging”

C.P. Bridge*, B.C. Bizzo*, J.M. Hillis, J.K. Chin, D.S. Comeau, R. Gauriau, F. Macruz, J. Pawar, F.T.C. Noro, E. Sharaf, M.S. Takahashi, B. Wright, J.F. Kalafut, K.P. Andriole, S.R. Pomerantz, S. Pedemonte, and R.G. González

*equal contribution

Scientific Reports 12, 2154 (2022)

[Nature]
“Deploying clinical machine learning? Consider the following...”

C. Lu, K. Chang, P. Singh, S. Pomerantz, S. Doyle, S. Kakarmath, C.P. Bridge, and J. Kalpathy-Cramer

Trustworthy AI For Healthcare Workshop, AAAI, 2022

[arXiv]
“A Fully Automated Deep Learning Pipeline for Multi-Vertebral Level Quantification and Characterization of Muscle and Adipose Tissue on Chest Computed Tomography”

C.P. Bridge*, T.D. Best*, M. Wrobel, J. Marquardt, K. Magudia, C. Javidan, J.H. Chung, J. Kalpathy-Cramer, K.P. Andriole, and F.J. Fintelmann

*equal contribution

Radiology: Artificial Intelligence, 2022 4:1

[RSNA]
“Computed Tomography-Based Body Composition Profile as a Screening Tool for Geriatric Frailty Detection”

O. Laur, M.J. Weaver, C.P. Bridge, E. Chow, M. Rosenthal, C.P. Bay, H. Javedan, M.B. Harris, and B. Khurana

Skeletal Radiology, December 2021

[Springer Link]
“Monte Carlo Dropout Increases Model Repeatability”

A. Lemay, K. Hoebel, C.P. Bridge, D. Egemen, A. Rodriguez, M. Schiffman, J. Campbell, and J. Kalpathy-Cramer

ML4H Workshop, NeurIPS 2021 (Extended abstract)

[arXiv] [Abstract]
“Basic Artificial Intelligence Techniques: Evaluation of Artificial Intelligence Performance”

J. Kalpathy-Cramer, J.B. Patel, C.P. Bridge, and K. Chang

Radiologic Clinics, Volume 59 Issue 6, P941-954, 2021

[Radiologic Clinics]
“The Trials and Tribulations of Assembling Large Medical Imaging Datasets for Machine Learning Applications”

K. Magudia, C.P. Bridge, K.P. Andriole, and M.H. Rosenthal

Journal of Digital Imaging, October 2021

JDI Best Scientific Paper Award

[Springer Link]
“Head CT Deep Learning Model for Early Stroke Identification Outperforms Human Experts”

B.C. Bizzo, R. Gauriau, D.S. Comeau, J.M. Hillis, C.P. Bridge, J.K. Chin, J. Pawar, A. Pourvaziri, I. Sesic, E. Sharaf, J. Cao, F.T.C. Noro, F. Kitamura, K. Dreyer, J.F. Kalafut, K.P. Andriole, S.R. Pomerantz, R.G. González, and M. Lev

Preprint in review

[Research Square]
“Traumatic Cervical Spine Fracture Patterns on CT: A Retrospective Analysis at a Level 1 Trauma center”

A. Tang, J. Pawar, C.P. Bridge, R. King, S. Kakarmath, M. Harris, and B. Khurana

Emergency Radiology, June 2021

[Springer Link]
“M-SiSSR: Regional Endocardial Function using Multilabel Simultaneous Subdivision Surface Registration”

D.M. Vigneault, F. Contijoch, C.P. Bridge, K. Lowe, C. Jan, and E.R. McVeigh

Functional Imaging and Modeling of the Heart, June 2021

[Springer Link]
“ECGAug: A Novel Method of Generating Augmented Annotated Electrocardiogram QRST Complexes and Rhythm Strips”

H.F. Stabeneau*, C.P. Bridge*, and J.W. Waks

Computers in Medicine and Biology, May 2021

*equal contribution

[Science Direct]
“Muscle Loss is Associated with Overall Survival in Patients with Metastatic Colorectal Cancer Independent of Tumor Mutational Status and Weight Loss”

T.D. Best, E.J. Roeland, N.K. Horick, E.E. Van Seventer, A. El-Jawahri, A.S. Troschel, P.C. Johnson, K.N. Kanter, M.G. Fish, J.P. Marquardt, C.P. Bridge, J.S. Temel, R.B. Corcoran, R.D. Nipp, and F.J. Fintelmann

The Oncologist, April 2021

[The Oncologist]
“Associations of Skeletal Muscle With Symptom Burden and Clinical Outcomes in Hospitalized Patients With Advanced Cancer”

E.E. van Seventer, J.-P. Marquardt, A.S. Troschel, T.D. Best, N. Horick, C. Azoba, R. Newcomb, E.J. Roeland, M. Rosenthal, C.P. Bridge, J.A. Greer, A. El-Jawahri, J. Temel, F.J. Fintelmann, and R.D. Nipp

Journal of the National Comprehensive Cancer Network, Volume 19: Issue 3

[JNCCN Open Access]
“Population-Scale CT-Based Body Composition Analysis Of a Large Outpatient Population Using Deep Learning To Derive Age, Sex, and Race-Specific Reference Curves”

K. Magudia, C.P. Bridge, C.P. Bay, A. Babic, F.J. Fintelmann, F. Troschel, N. Miskin, W. Wrobel, L.K. Brais, K.P. Andriole, B.M. Wolpin, and M.H. Rosenthal

Radiology, November 2020

[Radiology]
“Name That Manufacturer. Relating Image Acquisition Bias with Task Complexity When Training Deep Learning Models: Experiments on Head CT”

G.P. Biondetti, R. Gauriau, C.P. Bridge, C. Lu, and K.P. Andriole

Preprint in review

[arXiv]
“Multilevel Body Composition Analysis on Chest Computed Tomography Predicts Hospital Length of Stay and Complications Following Lobectomy for Lung Cancer: A Multicenter Study”

T.D. Best, S.F. Mercaldo, D.S. Bryan, J.-P. Marquardt, M.M. Wrobel, C.P. Bridge, F.M. Troschel, C. Javidan, J.H. Chung, A. Muniappan, S. Bhalla, B.F. Meyers, M.K. Ferguson, H.A. Gaissert, and F.J. Fintelmann

Annals of Surgery, July 2020

[Wolters Kluwer]
“Using DICOM Metadata for Radiological Image Series Categorizations: a Feasibility Study on Large Clinical Brain MRI Datasets”

R. Gauriau, C.P. Bridge, L. Chen, F. Kitamura, N.A. Tenenholtz, J.E. Kirsch, K.P. Andriole, M.H. Michalski, and B.C. Bizzo

Journal of Digital Imaging, January 2020

[Springer Link]
“Semi-Supervised Natural Language Processing Approach for Fine-Grained Classification of Medical Reports”

N. Deshmukh, S. Gumustop, R. Gauriau, V. Buch, B. Wright, C.P. Bridge, R. Naidu, K. Andriole, and B. Bizzo

New In ML Workshop, NeurIPS, Vancouver, December 2019

[arXiv]
“Fully-Automated Analysis of Body Composition from CT in Cancer Patients Using Convolutional Neural Networks”

C.P. Bridge*, M. Rosenthal*, B. Wright, G. Kotecha, F. Fintelmann, F. Troschel, N. Miskin, K. Desai, W. Wrobel, A. Babic, N. Khalaf, L. Brais, M. Welch, C. Zellers, N. Tenenholtz, M. Michalski, B. Wolpin, and K. Andriole

Workshop on Clinical Image-based Procedures, MICCAI, Granada 2018

*equal contribution

[Springer Link] [arXiv]
“Temporal HeartNet: Towards Human-Level Automatic Analysis of Fetal Cardiac Screening Video”

W. Huang, C.P. Bridge, J.A. Noble, and A. Zisserman

MICCAI, Québec City 2017, pp. 341-349

[Springer Link] [arXiv] [Slides] [Poster]
“Localizing Cardiac Structures in Fetal Heart Ultrasound Video”

C.P. Bridge, C. Ioannou, and J.A. Noble

Machine Learning in Medical Imaging Workshop, MICCAI, 2017, pp. 246-255

[Springer Link] [Authors’ Manuscript] [Poster]
“A Framework for Analysis of Linear Ultrasound Videos to Detect Fetal Presentation and Heartbeat”

M.A. Maraci, C.P. Bridge, R. Napolitano, A. Papageorghiou, and J.A. Noble

Medical Image Analysis, 37 (April 2017) pp. 22-36

[Open Access Paper]
“Automated Characterisation Of The Fetal Heart In Ultrasound Images Using Fully Convolutional Neural Networks”

V. Sundaresan, C.P. Bridge, C. Ioannou, and J.A. Noble

IEEE International Symposium on Biomedical Imaging, Melbourne, April 2017, pp. 671-674

[IEEE Xplore]
“Automated Annotation and Quantitative Description of Ultrasound Videos of the Fetal Heart”

C.P. Bridge, C. Ioannou, and J.A. Noble

Medical Image Analysis 36 (February 2017) pp. 147-161

[Open Access Paper] [Authors’ Manuscript]
“Object Localisation in Fetal Ultrasound Images Using Invariant Features”

C.P. Bridge and J.A. Noble

Proceedings of IEEE International Symposium on Biomedical Imaging, New York City, 2015

[IEEE Xplore] [Authors’ Manuscript] [Poster] (© IEEE 2015)

Conference Abstracts

“Deep Learning-Based Prediction of Immune Checkpoint Inhibitor Efficacy in Brain Metastases Using Brain MRI”

M.S. Guelen, M.C. Cleveland, J.B. Patel, T. Bodenmann, F.J. Dorfner, N. Gil, S. Bhat Brahmavar, D. Pulido-Arias, J. Kalpathy-Cramer, J.-P. Thiran, B.R. Rosen, E. Gerstner, J. Nawabi, D. Wasilewski, A. Dell’Orco, A. E. Kim, and C.P. Bridge

AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning, Montreal, July, 2025
“Enhancing Precision Oncology for Haitian Breast Cancer Patients Using Deep Learning-Enabled Histopathology Analysis”

D. Pulido-Arias*, R. Henderson*, J. Lormil, C. Millien, M. Djenane Jose, G. Flambert, J. Bontemps, E. Georges, M. Mathelier, M. Corrieulus, T. Goncalves, E. Gerstner, J. Kalpathy-Cramer, A. Brown, K. Landgraf, J. Jeronimo, P. Castle, J. Brock*, C.P. Bridge*, and A.E. Kim*

United States and Canadian Academy of Pathology, Boston, MA, 2025
“Dissecting the Impact of Data Augmentation on Whole Slide Image Classification”

D. Pulido-Arias, T.F. Gonçalves, J. Kalpathy-Cramer, E. Gerstner, J.S. Cardoso, A.E. Kim, and C.P. Bridge

Conference on Machine Intelligence in Medical Imaging (CMIMI), Boston, MA, 2024

[Abstract]
“CT-Based Radiomics Analysis Can Differentiate Adrenal Pheochromocytomas From Adenomas With High Accuracy: Results in 170 Masses”

D. Glazer, M. Viator, A. Sharp, J.B. Patel, C.P. Bridge, B.E. Dabiri, O.S. Pianykh, and W.W. Mayo-Smith

Radiological Society of North America Annual Meeting, Chicago, IL, 2024 (to appear)
“Association of Visceral Adiposity with Incident and Recurrent Diverticulitis in an Electronic Health Record-based Cohort Study”

J. Ha, C.P. Bridge, K.P Andriole, A. Kambadakone, F.J. Fintelmann, M.H. Rosenthal, R. Gollub, E.L. Giovannucci, L.L. Strate, W. Ma, and A.T. Chan

Digestive Disease Weekly, 2024
“Association of myosteatosis and sarcopenia on abdominal CT with the risk of incident colorectal cancer”

J. Ha, C.P. Bridge, A. Kambadakone, F.J. Fintelmann, M.H. Rosenthal, W. Ma, and A.T. Chan

American Roentgen Ray Society, 2024
“A Deep Learning Algorithm For Fully Automated Volumetric Measurement of Meningioma Burden”

M. Cleveland, A. Kim, J. Patel, O. McCall, W. Liu, S. Ahmed, B. Bearce, K. Chang, M. De Sauvage, K. Hoebel, J. Larson, N. Nayyar, D. Pulido, P. Singh, E. Summers, J. Kalpathy-Cramer, S.R. Plotkin, P. Brastianos, C.P. Bridge, and E.R. Gerstner

Neuro-oncology, 2023

[Abstract]
“ChatGPT Enhanced Radiology Reporting using PRECISE Framework For Patient-Centered Care”

S. Tripathi, E. Garza Frias, L. Mutter, M. Dezube, C.P. Bridge, and D. Daye

Conference on Machine Intelligence in Medical Imaging, 2023
“Artificial Intelligence-Based CT Body Composition improve Post-procedural Mortality Prediction in Patients undergoing Transjugular Intrahepatic Portosystemic Shunt (TIPS)”

T. Elhakim, J. Kondo, A. Mansur, O.M.F. Omar, A.A. Megahed, K. Ahmed, F.J. Fintelmann, E. Wehrenberg-Klee, C.P. Bridge, and D. Daye

Radiological Society of North America (RSNA) Annual Meeting, November 2023 (Scientific Presentation)
“Beyond MELD Score: Association of Sarcopenia with 90-Day Mortality Post Transjugular Intrahepatic Portosystemic Shunt (TIPS) Placement”

T. Elhakim, A. Mansur, J. Kondo, C. Suraci, O.M.F. Omar, F.J. Fintelmann, E. Wehrenberg-Klee, C.P. Bridge, and D. Daye

Society of Interventional Radiology (SIR) Annual Meeting, 2023

[Abstract]
“Normalization of Spinal MRI Series Types from DICOM Metadata”

S. Pomerantz, C.P. Bridge, C. Lu

Radiological Society of North America (RSNA) Annual Meeting, November 2022 (Scientific Presentation)
“Identification of Large Vessel Occlusion on CTA Head Using Artificial Intelligence”

J. Hillis, B. Bizzo, C. Lu, C.P. Bridge, J. Chin, S. Pomerantz, K. Dreyer, K. Andriole, M. Lev, R.G. Gonzalez

Neurology, May 2022, 98 (18 Supplement) 3424

[Abstract]
“Identification of Early Ischemia on Non-Contrast CT Head Using Artificial Intelligence”

J. Hillis, B. Bizzo, R. Gauriau, C.P. Bridge, J. Chin, S. Pomerantz, K. Dreyer, K. Andriole, M. Lev, R.G. Gonzalez

Neurology, May 2022, 98 (18 Supplement) 241

[Abstract]
“Is this good enough? On expert perception of brain tumor segmentation quality”

K. Hoebel, C.P. Bridge, S. Ahmed, O. Akintola, C. Chung, R. Huang, J. Johnson, A. Kim, K.I. Ly, K. Chang, J. Patel, M. Pinho, B. Rosen, E. Gerstner, and J. Kalpathy-Cramer

SPIE Medical Imaging, San Diego, California, February 2022

[Abstract]
“Do I know this? segmentation uncertainty under domain shift”

K. Hoebel, C.P. Bridge, A. Lemay, K. Chang, J. Patel, B. Rosen, and J. Kalpathy-Cramer

SPIE Medical Imaging, San Diego, California, February 2022

[Abstract]
“A Fully Automated Pipeline For Multi-vertebral Level Quantification And Characterization Of Muscle And Adipose Tissue On Chest Computed Tomography”

C.P. Bridge, T.D. Best, M. Wrobel, J. Marquardt, K. Magudia, J.H. Chung, M.H. Rosenthal, J. Kalpathy-Cramer, K.P. Andriole, and F.J. Fintelmann

Radiological Society of North America Annual Meeting, 1st December 2021

[Recorded Session (Paywall)]
“Teaching a Machine To Find Adrenal Glands: Novel Machine Learning Model For Adrenal Gland Segmentation and Mass Classification on CT”

C. Robinson-Weiss, J.B. Patel, B. Bizzo, D. Glazer, C.P. Bridge, B. Dabiri, K. Dreyer, K. Andriole, J. Kalpathy-Cramer, and W. Mayo-Smith

Radiological Society of North America Annual Meeting, November 2021
“Do you Agree? An Exploration of Inter-rater Variability and Deep Learning Segmentation Uncertainty”

K.V. Hoebel, C.P. Bridge, J.B. Patel, K. Chang, M.C. Pinho, X. Ma, B.R. Rosen, T.T. Batchelor, E.R. Gerstner, and J. Kalpathy-Cramer

International Society for Magnetic Resonance in Medicine Annual Meeting, May 2021
“Unified Universal Lesion Detector”

A. Vaswani, M. Aggarwal, N.T. Arun, S. Gupta, K. Chang, C.P. Bridge, J. Patel, K. Hoebel, M. Gidwani, J. Pawar, J. Kalpathy-Cramer, and P. Singh

IEEE International Symposium on Biomedical Imaging, April 2021
“Body Composition Profile of Clinically Frail Patients Based on Their Opportunistic Cross-sectional CT Imaging - A Pathway to Early Diagnosis and Treatment of Frailty”

O. Laur, C.P. Bridge, E. Chow, M.H. Rosenthal, C.P. Bay, H. Javedan, M. Harris, and B. Khurana

Radiological Society of North America (RSNA) Annual Meeting, November 2020 (On Demand Paper)
“Prediction of Major Cardiovascular Events in a Large Outpatient Adult Cohort Using Fully Automated and Normalized Deep Learning Body Composition Analysis of Routine Abdominal CT”

K. Magudia, C.P. Bridge, C.P. Bay, F.J. Fintelmann, A. Babic, K.P. Andriole, B. Wolpin, and M. Rosenthal

Radiological Society of North America (RSNA) Annual Meeting, November 2020 (Featured Paper)
“Representing and Communicating AI Model Results in Standard DICOM Format Using the Python Programming Language”

C.P. Bridge, S.W. Doyle, A.Y. Fedorov, S. Pieper, E. Ziegler, J. Petts, D.A. Clunie, G.J. Harris, K.P. Andriole, J.K. Lennerz, and M.D. Herrmann

Radiological Society of North America (RSNA) Annual Meeting, November 2020 (Educational Exhibit)

[Slides]
“Body Composition on Chest Computed Tomography Predicts Hospital Length of Stay and Complications Following Lobectomy for Lung Cancer: A Multicenter Study”

F. Fintelmann, T.D. Best, S.F. Mercaldo, J.-P. Marquardt, A. Muniappan, C.P. Bridge, F.M. Troschel, S. Bhalla, C. Javidan, D.S. Bryan, J.H. Chung, B.F. Meyers, H.A. Gaissert, and M.K. Ferguson

American Thoracic Society International Conference, May 2020

[Abstract]
“Relationships Among Skeletal Muscle, Symptom Burden, Health Care Utilization, and Survival in Hospitalized Patients with Advanced Cancer”

C. Azoba, E. van Seventer, J.P. Marquardt, A.S. Troschel, T.D. Best, N. Horick, R. Newcomb, E.J. Roeland, M. Rosenthal, C.P. Bridge, J. Greer, A. El-Jawahri, J. Temel, F.J. Fintelmann, and R.D. Nipp

American Society of Clinical Oncology Annual Meeting, 2020

[Abstract]
“Using Deep Learning to Aid Brown Fat Detection in F-18 FDG PET/CT”

E. Sun, C.P. Bridge, R.C. King, H. Hyun, and K.P. Andriole

Society for Imaging Informatics in Medicine Annual Meeting, Austin 2020

[Abstract]
“Name the Manufacturer: A Simple Experiment to Show Image Acquisition Bias When Training Deep Learning Models”

G.P. Biondetti, R. Gauriau, C. Lu, C.P. Bridge, and K.P. Andriole

Society for Imaging Informatics in Medicine Annual Meeting, Austin 2020
“Highdicom - High-Level DICOM Abstractions for the Python Programming Language to Encode Image-Derived Annotations and Machine Learning Outputs in Standard Format”

M.D. Herrmann, A. Fedorov, S. Pieper, S.W. Doyle, C.P. Bridge, D. Clunie, and J.K. Lennerz

Society for Imaging Informatics in Medicine Annual Meeting, Austin 2020

[Abstract]
“CT-based Body Composition Analysis: Comparison of Single-slice Versus Multi-slice Averaging for Estimation of Change Over Time”

K. Magudia, C.P. Bridge, F.J. Fintelmann, K. Andriole, and M. Rosenthal

Society of Abdominal Radiology Annual Meeting, Maui 2020
“The Trials and Tribulations of Assembling Large Datasets for Machine Learning Applications”

K. Magudia, C.P. Bridge, M. Walters, A. McCarthy, M. Michalski, K. Andriole, and M. Rosenthal

Society for Imaging Informatics in Medicine Annual Meeting, Denver 2019

[Abstract]
“Fully Automated Analysis of Body Composition from Routine Clinical Abdominal CT is Associated with Overall Survival in an Unselected Outpatient Population”

K. Magudia, C.P. Bridge, C.P. Bay, N. Tenenholtz, A. Babic, K.P. Andriole, B. Wolpin, and M. Rosenthal

Society of Abdominal Radiology Annual Meeting, Orlando 2019
“Fully Automated Analysis of Body Composition from Routine Clinical Abdominal CT: Quality Assurance and Failure Analysis”

K. Magudia, C.P. Bridge, C.P. Bay, N. Tenenholtz, A. Babic, K.P. Andriole, B. Wolpin, and M. Rosenthal

Society of Abdominal Radiology Annual Meeting, Orlando 2019
“Deep Learning for Acute Ischemic Stroke on Diffusion MRI: Performance Analysis in a Consecutive Cohort”

B. Bizzo, C.P. Bridge, R. Gauriau, W. Wiggins, M.T. Caton, J. Hillis, B. Wright, N. A. Tenenholtz, K.P. Andriole, M. Michalski, and G. Gonzalez

International Stroke Conference, Honolulu 2019

[AHA Journals]
“Deep Learning for Acute Ischemic Stroke on Diffusion-Weighted MR Imaging”

B. Bizzo, C.P. Bridge, S. Pedemonte, B. Wright, R.R. Almeida, S. Doyle, M. Walters, N. Tenenholtz, A. McCarthy, S. Pomerantz, K. Andriole, R. Gonzalez, and M. Michalski

Radiological Society of North America (RSNA) Annual Meeting, Chicago 2018
“Towards Automating the ISUOG ‘Six‐Step Basic Ultrasound’ Scan”

M.A. Maraci, C.P. Bridge, J.A. Noble, C. Aye, M. Molloholli, R. Napolitano, A.T. Papageorghiou

Abstracts of the 25th World Congress on Ultrasound in Obstetrics and Gynecology, Montreal 2015

[Wiley Online]

Patents and Patent Applications

“CTA Large Vessel Occlusion Model”

M.D. Herrmann, J.F. Kalafut, B.C. Bizzo, C.P. Bridge, M. Lev, C. Lu, and J.M. Hillis

US Patent Application 17/083,761

[Google Patents]
“Computed Tomography Medical Imaging Intracranial Hemorrhage Model”

J.F. Kalafut, B. Bizzo, B. Hashemian, C.P. Bridge, N. Tenenholtz, and S.R. Pomerantz

US Patent Application 16/587,828

[Free Patents Online]
“Medical Imaging Stroke Model”

J.F. Kalafut, B. Bizzo, S. Pedemonte, C.P. Bridge, N. Tenenholtz, and R.G. Gonzalez

US Patent Application 16/588,080

[PDF]

Other Resources

C.P. Bridge*, A. Fredriksson*, J. Guan, S. Bryson, J.M. Hillis, S. Mercaldo, R.J. Morley, M.D. Li, X. Li, E. L’Italien, D. Sack, A. Zhong, K.J. Dreyer, M. Flores, J. Kalpathy-Cramer, Q. Li, L.R. Lamb, C.D. Lehman, T. Schultz, K.P. Andriole, C. Compas, B.C. Bizzo*, and R. Haukioja*. “Large-Scale Model Validation in Healthcare.” (*co-first/last author) – White Paper, 2022. [Nvidia]
C.P. Bridge, “Computer-Aided Analysis of Fetal Cardiac Ultrasound Videos” – DPhil Thesis, University of Oxford, 2017 [PDF] [Oxford Research Archive]
C.P. Bridge, “An Introduction to the Monogenic Signal” – A tutorial on the monogenic signal. [arXiv] [PDF]
C.P. Bridge, “Registration of the Human Femur” – MEng Thesis, University of Cambridge, 2013 [PDF]

Peer Review

I have reviewed submissions for the following journals and conferences:

Publications and Activities

Journal and Conference Papers

“PRECISE Framework: Enhanced Radiology Reporting With GPT for Improved Readability, Reliability, and Patient-Centered Care”

“Using Interpretable Rule-Learning Artificial Intelligence to Optimally Differentiate Adrenal Pheochromocytomas from Adenomas with CT Radiomics”

“A Review of Deep Learning for Brain Tumor Analysis in MRI”

“Investigating the Potential of Diffusion Tensor Atlases to Generate Anisotropic Clinical Tumor Volumes in Glioblastoma Patients”

“Large Language Models as an Academic Resource for Radiologists Stepping into Artificial Intelligence Research”

“Comparing Commercial and Open-Source Large Language Models for Labeling Chest Radiograph Reports”

“Addressing Catastrophic Forgetting for Medical Domain Expansion”

“Beyond MELD Score: Association of Machine Learning-derived CT Body Composition with 90-Day Mortality Post Transjugular Intrahepatic Portosystemic Shunt Placement”

“Scalable Drift Monitoring in Medical Imaging AI”

“Multimodal Deep Learning-Based Prediction of Immune Checkpoint Inhibitor Efficacy in Brain Metastases”

“No Code Machine Learning: Validating the Approach on Use-Case for Classifying Clavicle Fractures”

“Diffusion Tensor Transformation for Personalizing Target Volumes in Radiation Therapy”

“Integrating Muscle Fiber Orientation from Visible Human Data into Radiotherapy Target Volumes”

“CASCADE: Context-Aware Data-Driven AI for Streamlined Multidisciplinary Tumor Board Recommendations in Oncology”

“Deep Learning-based Prediction of Breast Cancer Tumor and Immune Phenotypes from Histopathology”

“The Influence of Anisotropy on the Clinical Target Volume of Brain Tumor Patients”

“From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer”

“Enrichment of the NLST and NSCLC-Radiomics Computed Tomography Collections with AI-derived Annotations”

“Conformal Triage for Medical Imaging AI Deployment”

“A Deep Learning Based Framework for Joint Image Registration and Segmentation of Brain Metastases on Magnetic Resonance Imaging”

“Abnormal Vascular Structure and Function Within Brain Metastases is Linked to Pembrolizumab Resistance”

“Machine Learning–Based Detection of Sarcopenic Obesity and Association with Adverse Outcomes in Patients Undergoing Surgical Treatment for Spinal Metastases”

“Not without Context—A Multiple Methods Study on Evaluation and Correction of Automated Brain Tumor Segmentations by Experts”

“Point-of-care AI-Assisted Stepwise Ultrasound Pneumothorax Diagnosis”

“NCI Imaging Data Commons: Towards Transparency, Reproducibility, and Scalability in Imaging AI”

“Expert-Centered Evaluation of Deep Learning Algorithms for Brain Tumor Segmentation”

“CheXstray: A Real-Time Multi-Modal Monitoring Workflow for Medical Imaging AI”

“Body Composition and Lung Cancer-Associated Cachexia in TRACERx”

“Addressing the Challenges of Implementing Artificial Intelligence Tools in Clinical Practice: Principles From Experience”

“Role of Machine Learning-Based CT Body Composition in Risk Prediction and Prognostication: Current State and Future Directions”

“Enhanced Physician Performance When Using an Artificial Intelligence Model to Detect Ischemic Stroke on Computed Tomography”

“Head CT Deep Learning Model is Highly Accurate for Early Infarct Estimation”

“Improving Repeatability of Deep Learning Models with Monte Carlo Dropout”

“Machine Learning for Adrenal Gland Segmentation and Adrenal Masses on CT”

“Utility of Normalized Body Composition Areas, Derived From Outpatient Abdominal CT Using a Fully Automated Deep Learning Method, for Predicting Subsequent Cardiovascular Events”

“Highdicom: A Python Library for Standardized Encoding of Image Annotations and Machine Learning Model Outputs in Pathology and Radiology”

“Evaluating Frailty, Mortality, and Complications Associated with Metastatic Spine Tumor Surgery Using Machine Learning-Derived Body Composition Analysis”

“Development and Clinical Application of a Deep Learning Model to Identify Acute Infarct on Magnetic Resonance Imaging”

“Deploying clinical machine learning? Consider the following...”

“A Fully Automated Deep Learning Pipeline for Multi-Vertebral Level Quantification and Characterization of Muscle and Adipose Tissue on Chest Computed Tomography”

“Computed Tomography-Based Body Composition Profile as a Screening Tool for Geriatric Frailty Detection”

“Monte Carlo Dropout Increases Model Repeatability”

“Basic Artificial Intelligence Techniques: Evaluation of Artificial Intelligence Performance”

“The Trials and Tribulations of Assembling Large Medical Imaging Datasets for Machine Learning Applications”

“Head CT Deep Learning Model for Early Stroke Identification Outperforms Human Experts”

“Traumatic Cervical Spine Fracture Patterns on CT: A Retrospective Analysis at a Level 1 Trauma center”

“M-SiSSR: Regional Endocardial Function using Multilabel Simultaneous Subdivision Surface Registration”

“ECGAug: A Novel Method of Generating Augmented Annotated Electrocardiogram QRST Complexes and Rhythm Strips”

“Muscle Loss is Associated with Overall Survival in Patients with Metastatic Colorectal Cancer Independent of Tumor Mutational Status and Weight Loss”

“Associations of Skeletal Muscle With Symptom Burden and Clinical Outcomes in Hospitalized Patients With Advanced Cancer”

“Population-Scale CT-Based Body Composition Analysis Of a Large Outpatient Population Using Deep Learning To Derive Age, Sex, and Race-Specific Reference Curves”

“Name That Manufacturer. Relating Image Acquisition Bias with Task Complexity When Training Deep Learning Models: Experiments on Head CT”

“Multilevel Body Composition Analysis on Chest Computed Tomography Predicts Hospital Length of Stay and Complications Following Lobectomy for Lung Cancer: A Multicenter Study”

“Using DICOM Metadata for Radiological Image Series Categorizations: a Feasibility Study on Large Clinical Brain MRI Datasets”

“Semi-Supervised Natural Language Processing Approach for Fine-Grained Classification of Medical Reports”

“Fully-Automated Analysis of Body Composition from CT in Cancer Patients Using Convolutional Neural Networks”

“Temporal HeartNet: Towards Human-Level Automatic Analysis of Fetal Cardiac Screening Video”

“Localizing Cardiac Structures in Fetal Heart Ultrasound Video”

“A Framework for Analysis of Linear Ultrasound Videos to Detect Fetal Presentation and Heartbeat”

“Automated Characterisation Of The Fetal Heart In Ultrasound Images Using Fully Convolutional Neural Networks”

“Automated Annotation and Quantitative Description of Ultrasound Videos of the Fetal Heart”

“Object Localisation in Fetal Ultrasound Images Using Invariant Features”

Conference Abstracts

“Deep Learning-Based Prediction of Immune Checkpoint Inhibitor Efficacy in Brain Metastases Using Brain MRI”

“Enhancing Precision Oncology for Haitian Breast Cancer Patients Using Deep Learning-Enabled Histopathology Analysis”

“Dissecting the Impact of Data Augmentation on Whole Slide Image Classification”

“CT-Based Radiomics Analysis Can Differentiate Adrenal Pheochromocytomas From Adenomas With High Accuracy: Results in 170 Masses”

“Association of Visceral Adiposity with Incident and Recurrent Diverticulitis in an Electronic Health Record-based Cohort Study”

“Association of myosteatosis and sarcopenia on abdominal CT with the risk of incident colorectal cancer”

“A Deep Learning Algorithm For Fully Automated Volumetric Measurement of Meningioma Burden”

“ChatGPT Enhanced Radiology Reporting using PRECISE Framework For Patient-Centered Care”

“Artificial Intelligence-Based CT Body Composition improve Post-procedural Mortality Prediction in Patients undergoing Transjugular Intrahepatic Portosystemic Shunt (TIPS)”

“Beyond MELD Score: Association of Sarcopenia with 90-Day Mortality Post Transjugular Intrahepatic Portosystemic Shunt (TIPS) Placement”

“Normalization of Spinal MRI Series Types from DICOM Metadata”

“Identification of Large Vessel Occlusion on CTA Head Using Artificial Intelligence”

“Identification of Early Ischemia on Non-Contrast CT Head Using Artificial Intelligence”

“Is this good enough? On expert perception of brain tumor segmentation quality”

“Do I know this? segmentation uncertainty under domain shift”