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2020 – today
- 2024
[j106]Khaled Saab
, Siyi Tang, Mohamed Taha, Christopher Lee-Messer, Christopher Ré, Daniel L. Rubin:
Towards trustworthy seizure onset detection using workflow notes. npj Digit. Medicine 7(1) (2024)- [j105]Sarthak Pati, Sourav Kumar, Amokh Varma, Brandon Edwards, Charles Lu, Liangqiong Qu, Justin J. Wang, Anantharaman Lakshminarayanan, Shih-han Wang, Micah J. Sheller, Ken Chang, Praveer Singh, Daniel L. Rubin, Jayashree Kalpathy-Cramer, Spyridon Bakas:
Privacy preservation for federated learning in health care. Patterns 5(7): 100974 (2024) - [j104]Zexuan Ji, Xiao Ma, Theodore Leng, Daniel L. Rubin, Qiang Chen:
Mirrored X-Net: Joint classification and contrastive learning for weakly supervised GA segmentation in SD-OCT. Pattern Recognit. 153: 110507 (2024) - [j103]Nandita Bhaskhar, Daniel L. Rubin, Christopher Lee-Messer:
An Explainable and Actionable Mistrust Scoring Framework for Model Monitoring. IEEE Trans. Artif. Intell. 5(4): 1473-1485 (2024) - 2023
- [j102]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: 106594 (2023) - [j101]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: 104522 (2023) - [j100]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): 2071-2082 (2023) - [j99]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): 1932-1943 (2023) - [c82]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: 50-71 - [c81]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: 444-467 - [c80]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: 1-4 - [c79]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 - [c78]Ali Mirzazadeh, Florian Dubost, Maxwell Pike, Krish Maniar, Max Zuo, Christopher Lee-Messer, Daniel L. Rubin:
ATCON: Attention Consistency for Vision Models. WACV 2023: 1880-1889 - [c77]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: 1890-1899 - [i53]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 abs/2301.12636 (2023) - [i52]Khaled Saab, Siyi Tang, Mohamed Taha, Christopher Lee-Messer, Christopher Ré, Daniel L. Rubin:
Towards trustworthy seizure onset detection using workflow notes. CoRR abs/2306.08728 (2023) - 2022
- [j98]Amara Tariq, Omar Kallas, Patricia C. Balthazar, Scott Jeffery Lee, Terry Desser, Daniel L. Rubin, Judy Wawira Gichoya, Imon Banerjee:
Transfer language space with similar domain adaptation: a case study with hepatocellular carcinoma. J. Biomed. Semant. 13(1): 8 (2022) - [j97]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) - [j96]Josh Sanyal, Daniel L. Rubin, Imon Banerjee:
A weakly supervised model for the automated detection of adverse events using clinical notes. J. Biomed. Informatics 126: 103969 (2022) - [j95]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): 524-533 (2022) - [j94]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): 413 (2022) - [j93]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): 4635-4644 (2022) - [c76]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 Modeling and Explanation for Disease Trajectory Prediction. AMIA 2022 - [c75]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: 10051-10061 - [c74]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: 219-235 - [c73]Siyi Tang, Jared Dunnmon, Khaled Kamal Saab, Xuan Zhang, Qianying Huang, Florian Dubost, Daniel L. Rubin, Christopher Lee-Messer:
Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis. ICLR 2022 - [c72]Louis Blankemeier, Isabel Gallegos, Juan Manuel Zambrano Chaves, David J. Maron, Alexander T. Sandhu, Fátima Rodriguez, Daniel L. Rubin, Bhavik N. Patel, Marc H. Willis, Robert D. Boutin, Akshay S. Chaudhari:
Opportunistic Incidence Prediction of Multiple Chronic Diseases from Abdominal CT Imaging Using Multi-task Learning. MICCAI (8) 2022: 309-318 - [c71]Khaled Saab, Sarah M. Hooper, Mayee F. Chen, Michael Zhang, Daniel L. Rubin, Christopher Ré:
Reducing Reliance on Spurious Features in Medical Image Classification with Spatial Specificity. MLHC 2022: 760-784 - [i51]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 abs/2202.13618 (2022) - [i50]Siyi Tang, Amara Tariq, Jared Dunnmon, Umesh Sharma, Praneetha Elugunti, Daniel L. Rubin, Bhavik N. Patel, Imon Banerjee:
Multimodal spatiotemporal graph neural networks for improved prediction of 30-day all-cause hospital readmission. CoRR abs/2204.06766 (2022) - [i49]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 abs/2205.04044 (2022) - [i48]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 abs/2205.08576 (2022) - [i47]Jupinder Parmar, Khaled Saab, Brian Pogatchnik, Daniel L. Rubin, Christopher Ré:
The Importance of Background Information for Out of Distribution Generalization. CoRR abs/2206.08794 (2022) - [i46]Nandita Bhaskhar, Daniel L. Rubin, Christopher Lee-Messer:
TRUST-LAPSE: An Explainable & Actionable Mistrust Scoring Framework for Model Monitoring. CoRR abs/2207.11290 (2022) - [i45]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 abs/2208.11563 (2022) - [i44]Ali Mirzazadeh, Florian Dubost, Maxwell Pike, Krish Maniar, Max Zuo, Christopher Lee-Messer, Daniel L. Rubin:
ATCON: Attention Consistency for Vision Models. CoRR abs/2210.09705 (2022) - [i43]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 abs/2211.11176 (2022) - 2021
- [j92]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: 103656 (2021) - [j91]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: 101893 (2021) - [j90]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): 1081-1089 (2021) - [j89]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) - [j88]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): 3945-3954 (2021) - [c70]Thiago Santos, Omar Kallas, Janice M. Newsome, Daniel L. Rubin, Judy W. Gichoya, Imon Banerjee:
A Fusion NLP Model for the Inference of Standardized Thyroid Nodule Malignancy Scores from Radiology Report Text. AMIA 2021 - [c69]Jean-Benoit Delbrouck, Cassie Zhang, Daniel L. Rubin:
QIAI at MEDIQA 2021: Multimodal Radiology Report Summarization. BioNLP@NAACL-HLT 2021: 285-290 - [c68]Oliver Zhang, Jean-Benoit Delbrouck, Daniel L. Rubin:
Out of Distribution Detection for Medical Images. UNSURE/PIPPI@MICCAI 2021: 102-111 - [c67]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: 603-614 - [i42]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 abs/2102.01678 (2021) - [i41]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 abs/2103.13511 (2021) - [i40]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 abs/2104.08336 (2021) - [i39]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 abs/2105.08147 (2021) - [i38]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 abs/2106.02100 (2021) - [i37]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 abs/2106.06047 (2021) - [i36]Liangqiong Qu, Niranjan Balachandar, Miao Zhang, Daniel L. Rubin:
Handling Data Heterogeneity with Generative Replay in Collaborative Learning for Medical Imaging. CoRR abs/2106.13208 (2021) - [i35]Miao Zhang, Liangqiong Qu, Praveer Singh, Jayashree Kalpathy-Cramer, Daniel L. Rubin:
SplitAVG: A heterogeneity-aware federated deep learning method for medical imaging. CoRR abs/2107.02375 (2021) - [i34]Liangqiong Qu, Niranjan Balachandar, Daniel L. Rubin:
An Experimental Study of Data Heterogeneity in Federated Learning Methods for Medical Imaging. CoRR abs/2107.08371 (2021) - [i33]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 abs/2111.09461 (2021) - [i32]Yuyin Zhou, Shih-Cheng Huang, Jason Alan Fries, Alaa Youssef, Timothy J. Amrhein, Marcello Chang, Imon Banerjee, Daniel L. Rubin, Lei Xing, Nigam Shah, Matthew P. Lungren:
RadFusion: Benchmarking Performance and Fairness for Multimodal Pulmonary Embolism Detection from CT and EHR. CoRR abs/2111.11665 (2021) - [i31]Siddharth Sharma, Florian Dubost, Christopher Lee-Messer, Daniel L. Rubin:
Automated Detection of Patients in Hospital Video Recordings. CoRR abs/2111.14270 (2021) - 2020
- [j87]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): 700-708 (2020) - [j86]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): 1340 (2020) - [j85]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): 25-36 (2020) - [j84]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): 49-53 (2020) - [j83]Edson F. Luque, Nelson J. O. Miranda, Daniel L. Rubin, Dilvan A. Moreira:
Automatic Staging of Cancer Tumors Using AIM Image Annotations and Ontologies. J. Digit. Imaging 33(2): 287-303 (2020) - [j82]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) - [j81]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): 100019 (2020) - [j80]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): 2711-2717 (2020) - [j79]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): 3443-3455 (2020) - [c66]Jiaming Zeng, Imon Banerjee, Michael Francis Gensheimer, Daniel L. Rubin:
Cancer Treatment Classification with Electronic Medical Health Records (Student Abstract). AAAI 2020: 13981-13982 - [c65]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: 181-191 - [c64]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: 867-880 - [i30]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 abs/2001.09501 (2020) - [i29]Darvin Yi, Endre Grøvik, Michael Iv, Elizabeth Tong, Greg Zaharchuk, Daniel L. Rubin:
Random Bundle: Brain Metastases Segmentation Ensembling through Annotation Randomization. CoRR abs/2002.09809 (2020) - [i28]Blaine Rister, Daniel L. Rubin:
Probabilistic bounds on data sensitivity in deep rectifier networks. CoRR abs/2007.06192 (2020) - [i27]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 abs/2009.01871 (2020) - [i26]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 abs/2010.08006 (2020) - [i25]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 abs/2011.08096 (2020) - [i24]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 abs/2011.14101 (2020)
2010 – 2019
- 2019
- [j78]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: 79-88 (2019) - [j77]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: 102-111 (2019) - [j76]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) - [j75]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) - [j74]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): 544-553 (2019) - [j73]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) - [c63]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 - [c62]Ron C. Li, Imon Banerjee, Daniel L. Rubin, Jonathan H. Chen:
Detecting unanticipated actions downstream from clinical decision support: a data mining approach. AMIA 2019 - [c61]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: 565-568 - [c60]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: 109503H - [c59]Ali Hatamizadeh, Assaf Hoogi, Debleena Sengupta, Wuyue Lu, Brian Wilcox, Daniel L. Rubin, Demetri Terzopoulos:
Deep Active Lesion Segmentation. MLMI@MICCAI 2019: 98-105 - [c58]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: 811-819 - [i23]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 abs/1902.10700 (2019) - [i22]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 abs/1903.07988 (2019) - [i21]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 abs/1903.11101 (2019) - [i20]Assaf Hoogi, Brian Wilcox, Yachee Gupta, Daniel L. Rubin:
Self-Attention Capsule Networks for Image Classification. CoRR abs/1904.12483 (2019) - [i19]Ali Hatamizadeh, Assaf Hoogi, Debleena Sengupta, Wuyue Lu, Brian Wilcox, Daniel L. Rubin, Demetri Terzopoulos:
Deep Active Lesion Segmentation. CoRR abs/1908.06933 (2019) - [i18]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 abs/1908.09067 (2019) - [i17]Okyaz Eminaga, Yuri Tolkach, Christian Kunder, Mahmoud Abbas, Ryan Han, Rosalie Nolley, Axel Semjonow, Martin Boegemann, Sebastian Huss, Andreas M. Loening, Robert B. West, Geoffrey A. Sonn, Richard E. Fan, Olaf Bettendorf, James D. Brooks, Daniel L. Rubin:
Deep Learning for Prostate Pathology. CoRR abs/1910.04918 (2019) - [i16]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 abs/1910.09100 (2019) - [i15]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 abs/1912.08775 (2019) - [i14]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 abs/1912.11966 (2019) - 2018
- [j72]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: 167-175 (2018) - [j71]