Daniel L. Rubin

ORCID

Publication Activity (10 Years)

Years Active: 1999-2023
Publications (10 Years): 140

Top Venues

CoRR

AMIA

J. Digit. Imaging

J. Biomed. Informatics

Publications

Rui Yan, Liangqiong Qu, Qingyue Wei, Shih-Cheng Huang, Liyue Shen, Daniel L. Rubin, Lei Xing, Yuyin Zhou
Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging. IEEE Trans. Medical Imaging 42 (7) (2023)
Juanma Zambrano Chaves, Nandita Bhaskhar, Maayane Attias, Jean-Benoit Delbrouck, Daniel L. Rubin, Andreas M. Loening, Curtis P. Langlotz, Akshay Chaudhari
RaLEs: a Benchmark for Radiology Language Evaluations. NeurIPS (2023)
Rogier van der Sluijs, Nandita Bhaskhar, Daniel L. Rubin, Curtis P. Langlotz, Akshay S. Chaudhari
Exploring Image Augmentations for Siamese Representation Learning with Chest X-Rays. MIDL (2023)
Khaled Saab, Siyi Tang, Mohamed Taha, Christopher Lee-Messer, Christopher Ré, Daniel L. Rubin
Towards trustworthy seizure onset detection using workflow notes. CoRR (2023)
Amara Tariq, Siyi Tang, Hifza Sakhi, Leo Anthony Celi, Janice M. Newsome, Daniel L. Rubin, Hari Trivedi, Judy Gichoya, Bhavik N. Patel, Imon Banerjee
Graph-Based Fusion of Imaging and Non-Imaging Data for Disease Trajectory Prediction. NER (2023)
Siyi Tang, Jared A. Dunnmon, Liangqiong Qu, Khaled Kamal Saab, Tina Baykaner, Christopher Lee-Messer, Daniel L. Rubin
Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Models. CHIL (2023)
Okyaz Eminaga, Mahmoud Abbas, Jeanne Shen, Mark A. Laurie, James D. Brooks, Joseph C. Liao, Daniel L. Rubin
PlexusNet: A neural network architectural concept for medical image classification. Comput. Biol. Medicine 154 (2023)
Nandita Bhaskhar, Wui Ip, Jonathan H. Chen, Daniel L. Rubin
Clinical outcome prediction using observational supervision with electronic health records and audit logs. J. Biomed. Informatics 147 (2023)
Florian Dubost, Erin Hong, Siyi Tang, Nandita Bhaskhar, Christopher Lee-Messer, Daniel L. Rubin
Semi-Supervised Learning for Sparsely-Labeled Sequential Data: Application to Healthcare Video Processing. WACV (2023)
Rogier van der Sluijs, Nandita Bhaskhar, Daniel L. Rubin, Curtis P. Langlotz, Akshay Chaudhari
Exploring Image Augmentations for Siamese Representation Learning with Chest X-Rays. CoRR (2023)
Siyi Tang, Amara Tariq, Jared A. Dunnmon, Umesh Sharma, Praneetha Elugunti, Daniel L. Rubin, Bhavik N. Patel, Imon Banerjee
Predicting 30-Day All-Cause Hospital Readmission Using Multimodal Spatiotemporal Graph Neural Networks. IEEE J. Biomed. Health Informatics 27 (4) (2023)
Siyi Tang, Jared A. Dunnmon, Liangqiong Qu, Khaled Kamal Saab, Christopher Lee-Messer, Daniel L. Rubin
Spatiotemporal Modeling of Multivariate Signals With Graph Neural Networks and Structured State Space Models. CoRR (2022)
Nandita Bhaskhar, Daniel L. Rubin, Christopher Lee-Messer
TRUST-LAPSE: An Explainable & Actionable Mistrust Scoring Framework for Model Monitoring. CoRR (2022)
Rui Yan, Liangqiong Qu, Qingyue Wei, Shih-Cheng Huang, Liyue Shen, Daniel L. Rubin, Lei Xing, Yuyin Zhou
Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging. CoRR (2022)
Xiang Bai, Hanchen Wang, Liya Ma, Yongchao Xu, Jiefeng Gan, Ziwei Fan, Fan Yang, Ke Ma, Jiehua Yang, Song Bai, Chang Shu, Xinyu Zou, Renhao Huang, Changzheng Zhang, Xiaowu Liu, Dandan Tu, Chuou Xu, Wenqing Zhang, Xi Wang, Anguo Chen, Yu Zeng, Dehua Yang, Ming-Wei Wang, Nagaraj Holalkere, Neil J. Halin, Ihab R. Kamel, Jia Wu, Xuehua Peng, Xiang Wang, Jianbo Shao, Pattanasak Mongkolwat, Jianjun Zhang, Weiyang Liu, Michael Roberts, Zhongzhao Teng, Lucian Beer, Lorena Escudero Sanchez, Evis Sala, Daniel L. Rubin, Adrian Weller, Joan Lasenby, Chuansheng Zheng, Jianming Wang, Zhen Li, Carola Schönlieb, Tian Xia
Author Correction: Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence. Nat. Mach. Intell. 4 (4) (2022)
Minhaj Nur Alam, Rikiya Yamashita, Vignav Ramesh, Tejas Prabhune, Jennifer I. Lim, R. V. P. Chan, Joelle A. Hallak, Theodore Leng, Daniel L. Rubin
Contrastive learning-based pretraining improves representation and transferability of diabetic retinopathy classification models. CoRR (2022)
Jupinder Parmar, Khaled Saab, Brian Pogatchnik, Daniel L. Rubin, Christopher Ré
The Importance of Background Information for Out of Distribution Generalization. CoRR (2022)
Liangqiong Qu, Yuyin Zhou, Paul Pu Liang, Yingda Xia, Feifei Wang, Ehsan Adeli, Li Fei-Fei, Daniel L. Rubin
Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning. CVPR (2022)
Yan-Ran Wang, Liangqiong Qu, Natasha Diba Sheybani, Xiaolong Luo, Jiangshan Wang, Kristina Elizabeth Hawk, Ashok Joseph Theruvath, Sergios Gatidis, Xuerong Xiao, Allison Pribnow, Daniel L. Rubin, Heike E. Daldrup-Link
Masked Co-attentional Transformer reconstructs 100x ultra-fast/low-dose whole-body PET from longitudinal images and anatomically guided MRI. CoRR (2022)
Alexander S. Berdichevsky, Mor Peleg, Daniel L. Rubin
Supervised Machine Learning Algorithm for Detecting Consistency between Reported Findings and the Conclusions of Mammography Reports. CoRR (2022)
Assaf Hoogi, Brian Wilcox, Yachee Gupta, Daniel L. Rubin
Self-attention Capsule Network for Tissue Classification in Case of Challenging Medical Image Statistics. ECCV Workshops (3) (2022)
Miao Zhang, Liangqiong Qu, Praveer Singh, Jayashree Kalpathy-Cramer, Daniel L. Rubin
SplitAVG: A Heterogeneity-Aware Federated Deep Learning Method for Medical Imaging. IEEE J. Biomed. Health Informatics 26 (9) (2022)
Audrey Ha, Bao H. Do, Adam L. Bartret, Charles X. Fang, Albert Hsiao, Amelie M. Lutz, Imon Banerjee, Geoffrey M. Riley, Daniel L. Rubin, Kathryn J. Stevens, Erin Wang, Shannon Wang, Christopher F. Beaulieu, Brian Hurt
Automating Scoliosis Measurements in Radiographic Studies with Machine Learning: Comparing Artificial Intelligence and Clinical Reports. J. Digit. Imaging 35 (3) (2022)
Jon André Ottesen, Darvin Yi, Elizabeth Tong, Michael Iv, Anna Latysheva, Cathrine Saxhaug, Kari Dolven Jacobsen, Åslaug Helland, Kyrre Eeg Emblem, Daniel L. Rubin, Atle Bjørnerud, Greg Zaharchuk, Endre Grøvik
2.5D and 3D segmentation of brain metastases with deep learning on multinational MRI data. Frontiers Neuroinformatics 16 (2022)
Rikiya Yamashita, Jin Long, Snikitha Banda, Jeanne Shen, Daniel L. Rubin
Learning domain-agnostic visual representation for computational pathology using medically-irrelevant style transfer augmentation. CoRR (2021)
Siyi Tang, Jared A. Dunnmon, Khaled Saab, Xuan Zhang, Qianying Huang, Florian Dubost, Daniel L. Rubin, Christopher Lee-Messer
Automated Seizure Detection and Seizure Type Classification From Electroencephalography With a Graph Neural Network and Self-Supervised Pre-Training. CoRR (2021)
Vignav Ramesh, Blaine Rister, Daniel L. Rubin
COVID-19 Lung Lesion Segmentation Using a Sparsely Supervised Mask R-CNN on Chest X-rays Automatically Computed from Volumetric CTs. CoRR (2021)
Sharut Gupta, Praveer Singh, Ken Chang, Liangqiong Qu, Mehak Aggarwal, Nishanth Thumbavanam Arun, Ashwin Vaswani, Shruti Raghavan, Vibha Agarwal, Mishka Gidwani, Katharina Hoebel, Jay B. Patel, Charles Lu, Christopher P. Bridge, Daniel L. Rubin, Jayashree Kalpathy-Cramer
Addressing catastrophic forgetting for medical domain expansion. CoRR (2021)
Khaled Saab, Sarah M. Hooper, Nimit Sharad Sohoni, Jupinder Parmar, Brian Pogatchnik, Sen Wu, Jared A. Dunnmon, Hongyang R. Zhang, Daniel L. Rubin, Christopher Ré
Observational Supervision for Medical Image Classification Using Gaze Data. MICCAI (2) (2021)
Liangqiong Qu, Yuyin Zhou, Paul Pu Liang, Yingda Xia, Feifei Wang, Li Fei-Fei, Ehsan Adeli, Daniel L. Rubin
Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning. CoRR (2021)
Liangqiong Qu, Niranjan Balachandar, Miao Zhang, Daniel L. Rubin
Handling Data Heterogeneity with Generative Replay in Collaborative Learning for Medical Imaging. CoRR (2021)
Oliver Zhang, Jean-Benoit Delbrouck, Daniel L. Rubin
Out of Distribution Detection for Medical Images. UNSURE/PIPPI@MICCAI (2021)
Miao Zhang, Liangqiong Qu, Praveer Singh, Jayashree Kalpathy-Cramer, Daniel L. Rubin
SplitAVG: A heterogeneity-aware federated deep learning method for medical imaging. CoRR (2021)
Siddharth Sharma, Florian Dubost, Christopher Lee-Messer, Daniel L. Rubin
Automated Detection of Patients in Hospital Video Recordings. CoRR (2021)
Liangqiong Qu, Niranjan Balachandar, Daniel L. Rubin
An Experimental Study of Data Heterogeneity in Federated Learning Methods for Medical Imaging. CoRR (2021)
Rikiya Yamashita, Jin Long, Snikitha Banda, Jeanne Shen, Daniel L. Rubin
Learning Domain-Agnostic Visual Representation for Computational Pathology Using Medically-Irrelevant Style Transfer Augmentation. IEEE Trans. Medical Imaging 40 (12) (2021)
Florian Dubost, Khaled Kamal Saab, Erin Hong, Daniel Yang Fu, Max Pike, Siddharth Sharma, Siyi Tang, Nandita Bhaskhar, Christopher Lee-Messer, Daniel L. Rubin
Double Descent Optimization Pattern and Aliasing: Caveats of Noisy Labels. CoRR (2021)
Xiang Bai, Hanchen Wang, Liya Ma, Yongchao Xu, Jiefeng Gan, Ziwei Fan, Fan Yang, Ke Ma, Jiehua Yang, Song Bai, Chang Shu, Xinyu Zou, Renhao Huang, Changzheng Zhang, Xiaowu Liu, Dandan Tu, Chuou Xu, Wenqing Zhang, Xi Wang, Anguo Chen, Yu Zeng, Dehua Yang, Ming-Wei Wang, Nagaraj Holalkere, Neil J. Halin, Ihab R. Kamel, Jia Wu, Xuehua Peng, Xiang Wang, Jianbo Shao, Pattanasak Mongkolwat, Jianjun Zhang, Weiyang Liu, Michael Roberts, Zhongzhao Teng, Lucian Beer, Lorena Escudero Sanchez, Evis Sala, Daniel L. Rubin, Adrian Weller, Joan Lasenby, Chuangsheng Zheng, Jianming Wang, Zhen Li, Carola-Bibiane Schönlieb, Tian Xia
Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence. CoRR (2021)
Xiang Bai, Hanchen Wang, Liya Ma, Yongchao Xu, Jiefeng Gan, Ziwei Fan, Fan Yang, Ke Ma, Jiehua Yang, Song Bai, Chang Shu, Xinyu Zou, Renhao Huang, Changzheng Zhang, Xiaowu Liu, Dandan Tu, Chuou Xu, Wenqing Zhang, Xi Wang, Anguo Chen, Yu Zeng, Dehua Yang, Ming-Wei Wang, Nagaraj Holalkere, Neil J. Halin, Ihab R. Kamel, Jia Wu, Xuehua Peng, Xiang Wang, Jianbo Shao, Pattanasak Mongkolwat, Jianjun Zhang, Weiyang Liu, Michael Roberts, Zhongzhao Teng, Lucian Beer, Lorena Escudero Sanchez, Evis Sala, Daniel L. Rubin, Adrian Weller, Joan Lasenby, Chuansheng Zheng, Jianming Wang, Zhen Li, Carola Schönlieb, Tian Xia
Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence. Nat. Mach. Intell. 3 (12) (2021)
Endre Grøvik, Darvin Yi, Michael Iv, Elizabeth Tong, Line Brennhaug Nilsen, Anna Latysheva, Cathrine Saxhaug, Kari Dolven Jacobsen, Åslaug Helland, Kyrre Eeg Emblem, Daniel L. Rubin, Greg Zaharchuk
Handling missing MRI sequences in deep learning segmentation of brain metastases: a multicenter study. npj Digit. Medicine 4 (2021)
Jean-Benoit Delbrouck, Cassie Zhang, Daniel L. Rubin
QIAI at MEDIQA 2021: Multimodal Radiology Report Summarization. BioNLP@NAACL-HLT (2021)
Yuhan Zhang, Xiwei Zhang, Zexuan Ji, Sijie Niu, Theodore Leng, Daniel L. Rubin, Songtao Yuan, Qiang Chen
An integrated time adaptive geographic atrophy prediction model for SD-OCT images. Medical Image Anal. 68 (2021)
Rebecca Sawyer Lee, Jared A. Dunnmon, Ann He, Siyi Tang, Christopher Ré, Daniel L. Rubin
Comparison of segmentation-free and segmentation-dependent computer-aided diagnosis of breast masses on a public mammography dataset. J. Biomed. Informatics 113 (2021)
Siyi Tang, Amirata Ghorbani, Rikiya Yamashita, Sameer Rehman, Jared A. Dunnmon, James Y. Zou, Daniel L. Rubin
Data Valuation for Medical Imaging Using Shapley Value: Application on A Large-scale Chest X-ray Dataset. CoRR (2020)
Holger R. Roth, Ken Chang, Praveer Singh, Nir Neumark, Wenqi Li, Vikash Gupta, Sharut Gupta, Liangqiong Qu, Alvin Ihsani, Bernardo C. Bizzo, Yuhong Wen, Varun Buch, Meesam Shah, Felipe Kitamura, Matheus Mendonça, Vitor Lavor, Ahmed Harouni, Colin Compas, Jesse Tetreault, Prerna Dogra, Yan Cheng, Selnur Erdal, Richard D. White, Behrooz Hashemian, Thomas J. Schultz, Miao Zhang, Adam McCarthy, B. Min Yun, Elshaimaa Sharaf, Katharina Viktoria Hoebel, Jay B. Patel, Bryan Chen, Sean Ko, Evan Leibovitz, Etta D. Pisano, Laura Coombs, Daguang Xu, Keith J. Dreyer, Ittai Dayan, Ram C. Naidu, Mona Flores, Daniel L. Rubin, Jayashree Kalpathy-Cramer
Federated Learning for Breast Density Classification: A Real-World Implementation. DART/DCL@MICCAI (2020)
Holger R. Roth, Ken Chang, Praveer Singh, Nir Neumark, Wenqi Li, Vikash Gupta, Sharut Gupta, Liangqiong Qu, Alvin Ihsani, Bernardo C. Bizzo, Yuhong Wen, Varun Buch, Meesam Shah, Felipe Kitamura, Matheus Mendonça, Vitor Lavor, Ahmed Harouni, Colin Compas, Jesse Tetreault, Prerna Dogra, Yan Cheng, Selnur Erdal, Richard D. White, Behrooz Hashemian, Thomas J. Schultz, Miao Zhang, Adam McCarthy, B. Min Yun, Elshaimaa Sharaf, Katharina Viktoria Hoebel, Jay B. Patel, Bryan Chen, Sean Ko, Evan Leibovitz, Etta D. Pisano, Laura Coombs, Daguang Xu, Keith J. Dreyer, Ittai Dayan, Ram C. Naidu, Mona Flores, Daniel L. Rubin, Jayashree Kalpathy-Cramer
Federated Learning for Breast Density Classification: A Real-World Implementation. CoRR (2020)
Niranjan Balachandar, Ken Chang, Jayashree Kalpathy-Cramer, Daniel L. Rubin
Accounting for data variability in multi-institutional distributed deep learning for medical imaging. J. Am. Medical Informatics Assoc. 27 (5) (2020)
Sharut Gupta, Praveer Singh, Ken Chang, Mehak Aggarwal, Nishanth Thumbavanam Arun, Liangqiong Qu, Katharina Hoebel, Jay B. Patel, Mishka Gidwani, Ashwin Vaswani, Daniel L. Rubin, Jayashree Kalpathy-Cramer
The unreasonable effectiveness of Batch-Norm statistics in addressing catastrophic forgetting across medical institutions. CoRR (2020)
Edson F. Luque, Nelson Miranda, Daniel L. Rubin, Dilvan A. Moreira
Automatic Staging of Cancer Tumors Using AIM Image Annotations and Ontologies. J. Digit. Imaging 33 (2) (2020)
Niranjan Balachandar, Ken Chang, Jayashree Kalpathy-Cramer, Daniel L. Rubin
Corrigendum to: Accounting for data variability in multi-institutional distributed deep learning for medical imaging. J. Am. Medical Informatics Assoc. 27 (8) (2020)
Darvin Yi, Endre Grøvik, Michael Iv, Elizabeth Tong, Greg Zaharchuk, Daniel L. Rubin
Brain Metastasis Segmentation Network Trained with Robustness to Annotations with Multiple False Negatives. CoRR (2020)
David M. Cohn, Tarub S. Mabud, Victoria A. Arendt, Andre D. Souffrant, Gyeong S. Jeon, Xiao An, William T. Kuo, Daniel Y. Sze, Lawrence V. Hofmann, Daniel L. Rubin
Toward Data-Driven Learning Healthcare Systems in Interventional Radiology: Implementation to Evaluate Venous Stent Patency. J. Digit. Imaging 33 (1) (2020)
Blaine Rister, Daniel L. Rubin
Probabilistic bounds on data sensitivity in deep rectifier networks. CoRR (2020)
Nathaniel C. Swinburne, David S. Mendelson, Daniel L. Rubin
Advancing Semantic Interoperability of Image Annotations: Automated Conversion of Non-standard Image Annotations in a Commercial PACS to the Annotation and Image Markup. J. Digit. Imaging 33 (1) (2020)
Florian Dubost, Erin Hong, Daniel Y. Fu, Nandita Bhaskhar, Siyi Tang, Khaled Saab, Daniel L. Rubin, Jared Dunnmon, Christopher Lee-Messer
Let's Hope it Works! Inaccurate Supervision of Neural Networks with Incorrect Labels: Application to Epilepsy. CoRR (2020)
Khaled Saab, Jared Dunnmon, Christopher Ré, Daniel L. Rubin, Christopher Lee-Messer
Weak supervision as an efficient approach for automated seizure detection in electroencephalography. npj Digit. Medicine 3 (2020)
Xiao Ma, Zexuan Ji, Sijie Niu, Theodore Leng, Daniel L. Rubin, Qiang Chen
MS-CAM: Multi-Scale Class Activation Maps for Weakly-Supervised Segmentation of Geographic Atrophy Lesions in SD-OCT Images. IEEE J. Biomed. Health Informatics 24 (12) (2020)
Darvin Yi, Endre Grøvik, Michael Iv, Elizabeth Tong, Greg Zaharchuk, Daniel L. Rubin
Random Bundle: Brain Metastases Segmentation Ensembling through Annotation Randomization. CoRR (2020)
Darvin Yi, Endre Grøvik, Michael Iv, Elizabeth Tong, Greg Zaharchuk, Daniel L. Rubin
Brain Metastasis Segmentation Network Trained with Robustness to Annotations with Multiple False Negatives. MIDL (2020)
Assaf Hoogi, Arjun Mishra, Francisco Gimenez, Jeffrey Dong, Daniel L. Rubin
Natural Language Generation Model for Mammography Reports Simulation. IEEE J. Biomed. Health Informatics 24 (9) (2020)
Jared A. Dunnmon, Alexander J. Ratner, Khaled Saab, Nishith Khandwala, Matthew Markert, Hersh Sagreiya, Roger E. Goldman, Christopher Lee-Messer, Matthew P. Lungren, Daniel L. Rubin, Christopher Ré
Cross-Modal Data Programming Enables Rapid Medical Machine Learning. Patterns 1 (2) (2020)
Khaled Saab, Jared Dunnmon, Roger E. Goldman, Alexander Ratner, Hersh Sagreiya, Christopher Ré, Daniel L. Rubin
Doubly Weak Supervision of Deep Learning Models for Head CT. MICCAI (3) (2019)
Okyaz Eminaga, Mahmoud Abbas, Yuri Tolkach, Rosalie Nolley, Christian Kunder, Axel Semjonow, Martin Boegemann, Andreas M. Loening, James D. Brooks, Daniel L. Rubin
Biologic and Prognostic Feature Scores from Whole-Slide Histology Images Using Deep Learning. CoRR (2019)
Imon Banerjee, Selen Bozkurt, Emel Alkim, Hersh Sagreiya, Allison W. Kurian, Daniel L. Rubin
Automatic inference of BI-RADS final assessment categories from narrative mammography report findings. J. Biomed. Informatics 92 (2019)
Assaf Hoogi, Brian Wilcox, Yachee Gupta, Daniel L. Rubin
Self-Attention Capsule Networks for Image Classification. CoRR (2019)
Ali Hatamizadeh, Assaf Hoogi, Debleena Sengupta, Wuyue Lu, Brian Wilcox, Daniel L. Rubin, Demetri Terzopoulos
Deep Active Lesion Segmentation. CoRR (2019)
Darvin Yi, Endre Grøvik, Michael Iv, Elisabeth Tong, Kyrre Eeg Emblem, Line Brennhaug Nilsen, Cathrine Saxhaug, Anna Latysheva, Kari Dolven Jacobsen, Åslaug Helland, Greg Zaharchuk, Daniel L. Rubin
MRI Pulse Sequence Integration for Deep-Learning Based Brain Metastasis Segmentation. CoRR (2019)
Jared Dunnmon, Alexander Ratner, Nishith Khandwala, Khaled Saab, Matthew Markert, Hersh Sagreiya, Roger E. Goldman, Christopher Lee-Messer, Matthew P. Lungren, Daniel L. Rubin, Christopher Ré
Cross-Modal Data Programming Enables Rapid Medical Machine Learning. CoRR (2019)
Rongbin Xu, Sijie Niu, Qiang Chen, Zexuan Ji, Daniel L. Rubin, Yuehui Chen
Automated geographic atrophy segmentation for SD-OCT images based on two-stage learning model. Comput. Biol. Medicine 105 (2019)
Selen Bozkurt, Emel Alkim, Imon Banerjee, Daniel L. Rubin
Automated Detection of Measurements and Their Descriptors in Radiology Reports Using a Hybrid Natural Language Processing Algorithm. J. Digit. Imaging 32 (4) (2019)
Imon Banerjee, Miji Sofela, Timothy Amrhein, Daniel L. Rubin, Roham Zamanian, Matthew P. Lungren
Prediction of Imaging Outcomes from Electronic Health Records: Pulmonary Embolism Case-Study. AMIA (2019)
Okyaz Eminaga, Mahmoud Abbas, Christian Kunder, Andreas M. Loening, Jeanne Shen, James D. Brooks, Curtis P. Langlotz, Daniel L. Rubin
Plexus Convolutional Neural Network (PlexusNet): A novel neural network architecture for histologic image analysis. CoRR (2019)
Ali Hatamizadeh, Assaf Hoogi, Debleena Sengupta, Wuyue Lu, Brian Wilcox, Daniel L. Rubin, Demetri Terzopoulos
Deep Active Lesion Segmentation. MLMI@MICCAI (2019)
Eli M. Cahan, Tina Hernandez-Boussard, Sonoo Thadaney Israni, Daniel L. Rubin
Putting the data before the algorithm in big data addressing personalized healthcare. npj Digit. Medicine 2 (2019)
Guillaume Vanoost, Yashin Dicente Cid, Daniel L. Rubin, Adrien Depeursinge
A lung graph model for the classification of interstitial lung diseases on CT images. Medical Imaging: Computer-Aided Diagnosis (2019)
Endre Grøvik, Darvin Yi, Michael Iv, Elizabeth Tong, Line Brennhaug Nilsen, Anna Latysheva, Cathrine Saxhaug, Kari Dolven Jacobsen, Åslaug Helland, Kyrre Eeg Emblem, Daniel L. Rubin, Greg Zaharchuk
Handling Missing MRI Input Data in Deep Learning Segmentation of Brain Metastases: A Multi-Center Study. CoRR (2019)
Imon Banerjee, Luis de Sisternes, Joelle A. Hallak, Theodore Leng, Aaron Osborne, Mary Durbin, Daniel L. Rubin
A Deep-learning Approach for Prognosis of Age-Related Macular Degeneration Disease using SD-OCT Imaging Biomarkers. CoRR (2019)
Yuhan Zhang, Zexuan Ji, Sijie Niu, Theodore Leng, Daniel L. Rubin, Qiang Chen
A Multi-Scale Deep Convolutional Neural Network For Joint Segmentation And Prediction Of Geographic Atrophy In SD-OCT Images. ISBI (2019)
Endre Grøvik, Darvin Yi, Michael Iv, Elisabeth Tong, Daniel L. Rubin, Greg Zaharchuk
Deep Learning Enables Automatic Detection and Segmentation of Brain Metastases on Multi-Sequence MRI. CoRR (2019)
Imon Banerjee, Yuan Ling, Matthew C. Chen, Sadid A. Hasan, Curtis P. Langlotz, Nathaniel Moradzadeh, Brian E. Chapman, Timothy Amrhein, David A. Mong, Daniel L. Rubin, Oladimeji Farri, Matthew P. Lungren
Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification. Artif. Intell. Medicine 97 (2019)
Okyaz Eminaga, Yuri Tolkach, Christian Kunder, Mahmoud Abbas, Ryan Han, Rosalie Nolley, Axel Semjonow, Martin Boegemann, Sebastian Huss, Andreas M. Loening, Robert West, Geoffrey A. Sonn, Richard E. Fan, Olaf Bettendorf, James D. Brooks, Daniel L. Rubin
Deep Learning for Prostate Pathology. CoRR (2019)
Menglin Wu, Xinxin Cai, Qiang Chen, Zexuan Ji, Sijie Niu, Theodore Leng, Daniel L. Rubin, Hyunjin Park
Geographic atrophy segmentation in SD-OCT images using synthesized fundus autofluorescence imaging. Comput. Methods Programs Biomed. 182 (2019)
Sebastian Echegaray, Shaimaa Bakr, Daniel L. Rubin, Sandy Napel
Quantitative Image Feature Engine (QIFE): an Open-Source, Modular Engine for 3D Quantitative Feature Extraction from Volumetric Medical Images. J. Digit. Imaging 31 (4) (2018)
Imon Banerjee, Matthew C. Chen, Matthew P. Lungren, Daniel L. Rubin
Radiology report annotation using intelligent word embeddings: Applied to multi-institutional chest CT cohort. J. Biomed. Informatics 77 (2018)
Kun-Hsing Yu, Gerald J. Berry, Daniel L. Rubin, Christopher Ré, Russ B. Altman, Michael Snyder
Unraveling the Molecular Basis of Lung Adenocarcinoma Dedifferentiation and Prognosis by Integrating Omics and Histopathology. AMIA (2018)
Imon Banerjee, Hailey H. Choi, Terry S. Desser, Daniel L. Rubin
A Scalable Machine Learning Approach for Inferring Probabilistic US-LI-RADS Categorization. AMIA (2018)
Myra Cheng, Alfiia Galimzianova, Ziga Lesjak, Ziga Spiclin, Christopher B. Lock, Daniel L. Rubin
A Multi-scale Multiple Sclerosis Lesion Change Detection in a Multi-sequence MRI. DLMIA/ML-CDS@MICCAI (2018)
Bethany Percha, Yuhao Zhang, Selen Bozkurt, Daniel L. Rubin, Russ B. Altman, Curtis P. Langlotz
Expanding a radiology lexicon using contextual patterns in radiology reports. J. Am. Medical Informatics Assoc. 25 (6) (2018)
Selen Bozkurt, Jung Park In, Kathleen Mary Kan, Michelle Ferrari, Daniel L. Rubin, James D. Brooks, Tina Hernandez-Boussard
An Automated Feature Engineering for Digital Rectal Examination Documentation using Natural Language Processing. AMIA (2018)
Ken Chang, Niranjan Balachandar, Carson K. Lam, Darvin Yi, James M. Brown, Andrew Beers, Bruce R. Rosen, Daniel L. Rubin, Jayashree Kalpathy-Cramer
Distributed deep learning networks among institutions for medical imaging. J. Am. Medical Informatics Assoc. 25 (8) (2018)
Imon Banerjee, Camille Kurtz, Alon Edward Devorah, Bao H. Do, Daniel L. Rubin, Christopher F. Beaulieu
Relevance feedback for enhancing content based image retrieval and automatic prediction of semantic image features: Application to bone tumor radiographs. J. Biomed. Informatics 84 (2018)
Imon Banerjee, Alexis Crawley, Mythili Bhethanabotla, Heike E. Daldrup-Link, Daniel L. Rubin
Transfer learning on fused multiparametric MR images for classifying histopathological subtypes of rhabdomyosarcoma. Comput. Medical Imaging Graph. 65 (2018)
Blaine Rister, Darvin Yi, Kaushik Shivakumar, Tomomi Nobashi, Daniel L. Rubin
CT organ segmentation using GPU data augmentation, unsupervised labels and IOU loss. CoRR (2018)
Anupama Gupta, Imon Banerjee, Daniel L. Rubin
Automatic information extraction from unstructured mammography reports using distributed semantics. J. Biomed. Informatics 78 (2018)
Imon Banerjee, Michael Francis Gensheimer, Douglas J. Wood, Solomon Henry, Daniel T. Chang, Daniel L. Rubin
Probabilistic Prognostic Estimates of Survival in Metastatic Cancer Patients (PPES-Met) Utilizing Free-Text Clinical Narratives. CoRR (2018)
Imon Banerjee, Hailey H. Choi, Terry S. Desser, Daniel L. Rubin
A Scalable Machine Learning Approach for Inferring Probabilistic US-LI-RADS Categorization. CoRR (2018)
Hakan Bulu, Dorothy A. Sippo, Janie M. Lee, Elizabeth S. Burnside, Daniel L. Rubin
Proposing New RadLex Terms by Analyzing Free-Text Mammography Reports. J. Digit. Imaging 31 (5) (2018)
Daniel J. Vreeman, Swapna Abhyankar, Kenneth C. Wang, Chris Carr, Beverly Collins, Daniel L. Rubin, Curtis P. Langlotz
The LOINC RSNA radiology playbook - a unified terminology for radiology procedures. J. Am. Medical Informatics Assoc. 25 (7) (2018)
Assaf Hoogi, Arjun Subramaniam, Rishi Veerapaneni, Daniel L. Rubin
Adaptive Estimation of Active Contour Parameters Using Convolutional Neural Networks and Texture Analysis. IEEE Trans. Medical Imaging 36 (3) (2017)
Zeshan Hussain, Francisco Gimenez, Darvin Yi, Daniel L. Rubin
Differential Data Augmentation Techniques for Medical Imaging Classification Tasks. AMIA (2017)

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Daniel L. Rubin