C. Bridge

发表

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

Katherine P. Andriole, Michael Rosenthal, Brian Wolpin, 2018, OR 2.0/CARE/CLIP/ISIC@MICCAI.

Evaluating frailty, mortality, and complications associated with metastatic spine tumor surgery using machine learning-derived body composition analysis.

Katherine P. Andriole, Mitchell S. Fourman, M. Bilsky, 2022, Journal of neurosurgery. Spine.

Highdicom: A Python library for standardized encoding of image annotations and machine learning model outputs in pathology and radiology

Jayashree Kalpathy-Cramer, S. Pieper, J. Lennerz, 2021, ArXiv.

Deploying clinical machine learning? Consider the following pdf

Jayashree Kalpathy-Cramer, Sujay Kakarmath, Christopher Bridge, 2021, ArXiv.

Computed tomography-based body composition profile as a screening tool for geriatric frailty detection

M. Harris, C. Bay, M. Weaver, 2021, Skeletal Radiology.

Population-Scale CT-based Body Composition Analysis of a Large Outpatient Population Using Deep Learning to Derive Age-, Sex-, and Race-specific Reference Curves.

Fabian M. Troschel, K. Andriole, B. Wolpin, 2020, Radiology.

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

J. Temel, N. Horick, J. Greer, 2021, Journal of the National Comprehensive Cancer Network : JNCCN.

Monte Carlo dropout increases model repeatability pdf

Jayashree Kalpathy-Cramer, Christopher P. Bridge, Katharina Hoebel, 2021, ArXiv.

Head CT deep learning model is highly accurate for early infarct estimation

Katherine P. Andriole, Walter F. Wiggins, F. Kitamura, 2023, Scientific Reports.

Improving the repeatability of deep learning models with Monte Carlo dropout

Jayashree Kalpathy-Cramer, A. Rodriguez, M. Schiffman, 2022, npj Digital Medicine.

Traumatic cervical spine fracture patterns on CT: a retrospective analysis at a level 1 trauma center

M. Harris, B. Khurana, Sujay S Kakarmath, 2021, Emergency Radiology.

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

Katherine P. Andriole, B. Wolpin, C. Bay, 2022, AJR. American journal of roentgenology.

A Fully Automated Deep Learning Pipeline for Multi-Vertebral Level Quantification and Characterization of Muscle and Adipose Tissue on Chest CT Scans.

Katherine P. Andriole, Jonathan H. Chung, Jayashree Kalpathy-Cramer, 2022, Radiology. Artificial intelligence.

Multilevel Body Composition Analysis on Chest Computed Tomography Predicts Hospital Length of Stay and Complications After Lobectomy for Lung Cancer

Jonathan H. Chung, Fabian M. Troschel, B. Meyers, 2020, Annals of surgery.

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

J. Temel, N. Horick, A. El-Jawahri, 2021, The oncologist.

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

Katherine P. Andriole, K. Magudia, M. Rosenthal, 2021, Journal of Digital Imaging.

"Name that manufacturer". Relating image acquisition bias with task complexity when training deep learning models: experiments on head CT pdf

Romane Gauriau, Christopher P. Bridge, Katherine P. Andriole, 2020, ArXiv.

Computer-aided analysis of fetal cardiac ultrasound videos

Christopher P. Bridge, C. Bridge, 2017 .

Body composition and lung cancer-associated cachexia in TRACERx

Angela E. Leek, N. Perrimon, Z. Szallasi, 2023, Nature Medicine.

Automated characterization of the fetal heart in ultrasound images using fully convolutional neural networks

Vaanathi Sundaresan, J. Alison Noble, Christopher P. Bridge, 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).

Automated annotation and quantitative description of ultrasound videos of the fetal heart

J. Alison Noble, Christopher P. Bridge, Christos Ioannou, 2017, Medical Image Anal..

Addressing catastrophic forgetting for medical domain expansion pdf

Daniel L. Rubin, Jayashree Kalpathy-Cramer, Mishka Gidwani, 2021, ArXiv.

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

Andrew Zisserman, Weilin Huang, J. Alison Noble, 2017, MICCAI.

A framework for analysis of linear ultrasound videos to detect fetal presentation and heartbeat

J. Alison Noble, Aris Papageorghiou, Raffaele Napolitano, 2017, Medical Image Anal..

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

Lina Chen, Romane Gauriau, Neil A. Tenenholtz, 2020, Journal of Digital Imaging.

Current State of Community-Driven Radiological AI Deployment in Medical Imaging

Brad W. Genereaux, M. Lungren, S. Ourselin, 2022, ArXiv.

Development and clinical application of a deep learning model to identify acute infarct on magnetic resonance imaging

Stefano Pedemonte, Romane Gauriau, Katherine P. Andriole, 2021, Scientific Reports.

ECGAug: A novel method of generating augmented annotated electrocardiogram QRST complexes and rhythm strips

Jonathan W. Waks, Christopher P. Bridge, Hans Friedrich Stabenau, 2021, Comput. Biol. Medicine.

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

Romane Gauriau, Christopher Bridge, James Hillis, 2021 .

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

Jayashree Kalpathy-Cramer, S. Pieper, J. Lennerz, 2021, Journal of Digital Imaging.