Daniel L. Rubin
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
54
Venues
13
Active years
1999–2024
Best venue rank
A*
Where they publish
Papers
54 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2024 | MICCAI | Addressing Catastrophic Forgetting by Modulating Global Batch Normalization Statistics for Medical Domain Expansion. | Sharut Gupta, Ken Chang, Liangqiong Qu, Aakanksha Rana, Syed Rakin Ahmed, 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, Praveer Singh |
| 2023 | WACV | Semi-Supervised Learning for Sparsely-Labeled Sequential Data: Application to Healthcare Video Processing. | Florian Dubost, Erin Hong, Siyi Tang, Nandita Bhaskhar, Christopher Lee-Messer, Daniel L. Rubin |
| 2023 | WACV | ATCON: Attention Consistency for Vision Models. | Ali Mirzazadeh, Florian Dubost, Maxwell Pike, Krish Maniar, Max Zuo, Christopher Lee-Messer, Daniel L. Rubin |
| 2022 | AMIA | Graph-based Fusion Modeling and Explanation for Disease Trajectory Prediction. | Amara Tariq, Siyi Tang, Hifza Sakhi, Leo Anthony Celi, Janice M. Newsome, Daniel L. Rubin, Hari Trivedi, Judy Gichoya, Bhavik N. Patel, Imon Banerjee |
| 2022 | CVPR | Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning. | Liangqiong Qu, Yuyin Zhou, Paul Pu Liang, Yingda Xia, Feifei Wang, Ehsan Adeli, Li Fei-Fei, Daniel L. Rubin |
| 2022 | ECCV | Self-attention Capsule Network for Tissue Classification in Case of Challenging Medical Image Statistics. | Assaf Hoogi, Brian Wilcox, Yachee Gupta, Daniel L. Rubin |
| 2022 | ICLR | Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis. | Siyi Tang, Jared Dunnmon, Khaled Kamal Saab, Xuan Zhang, Qianying Huang, Florian Dubost, Daniel L. Rubin, Christopher Lee-Messer |
| 2022 | MICCAI | Opportunistic Incidence Prediction of Multiple Chronic Diseases from Abdominal CT Imaging Using Multi-task Learning. | Louis Blankemeier, Isabel Gallegos, Juan Manuel Zambrano Chaves, David J. Maron, Alexander T. Sandhu, Ftima Rodriguez, Daniel L. Rubin, Bhavik N. Patel, Marc H. Willis, Robert D. Boutin, Akshay S. Chaudhari |
| 2021 | AMIA | A Fusion NLP Model for the Inference of Standardized Thyroid Nodule Malignancy Scores from Radiology Report Text. | Thiago Santos, Omar Kallas, Janice M. Newsome, Daniel L. Rubin, Judy W. Gichoya, Imon Banerjee |
| 2021 | MICCAI | Observational Supervision for Medical Image Classification Using Gaze Data. | Khaled Saab, Sarah M. Hooper, Nimit Sharad Sohoni, Jupinder Parmar, Brian Pogatchnik, Sen Wu, Jared A. Dunnmon, Hongyang R. Zhang, Daniel L. Rubin, Christopher R |
| 2021 | MICCAI | Out of Distribution Detection for Medical Images. | Oliver Zhang, Jean-Benoit Delbrouck, Daniel L. Rubin |
| 2020 | AAAI | Cancer Treatment Classification with Electronic Medical Health Records (Student Abstract). | Jiaming Zeng, Imon Banerjee, Michael Francis Gensheimer, Daniel L. Rubin |
| 2020 | MICCAI | Federated Learning for Breast Density Classification: A Real-World Implementation. | 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 Mendona, 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 |
| 2019 | AMIA | Prediction of Imaging Outcomes from Electronic Health Records: Pulmonary Embolism Case-Study. | Imon Banerjee, Miji Sofela, Timothy Amrhein, Daniel L. Rubin, Roham Zamanian, Matthew P. Lungren |
| 2019 | AMIA | Detecting unanticipated actions downstream from clinical decision support: a data mining approach. | Ron C. Li, Imon Banerjee, Daniel L. Rubin, Jonathan H. Chen |
| 2019 | MICCAI | Deep Active Lesion Segmentation. | Ali Hatamizadeh, Assaf Hoogi, Debleena Sengupta, Wuyue Lu, Brian Wilcox, Daniel L. Rubin, Demetri Terzopoulos |
| 2019 | MICCAI | Doubly Weak Supervision of Deep Learning Models for Head CT. | Khaled Saab, Jared Dunnmon, Roger E. Goldman, Alexander Ratner, Hersh Sagreiya, Christopher R, Daniel L. Rubin |
| 2018 | AMIA | A Scalable Machine Learning Approach for Inferring Probabilistic US-LI-RADS Categorization. | Imon Banerjee, Hailey H. Choi, Terry S. Desser, Daniel L. Rubin |
| 2018 | AMIA | An Automated Feature Engineering for Digital Rectal Examination Documentation using Natural Language Processing. | Selen Bozkurt, Jung Park In, Kathleen Mary Kan, Michelle Ferrari, Daniel L. Rubin, James D. Brooks, Tina Hernandez-Boussard |
| 2018 | AMIA | Unraveling the Molecular Basis of Lung Adenocarcinoma Dedifferentiation and Prognosis by Integrating Omics and Histopathology. | Kun-Hsing Yu, Gerald J. Berry, Daniel L. Rubin, Christopher R, Russ B. Altman, Michael Snyder |
| 2018 | MICCAI | A Multi-scale Multiple Sclerosis Lesion Change Detection in a Multi-sequence MRI. | Myra Cheng, Alfiia Galimzianova, Ziga Lesjak, Ziga Spiclin, Christopher B. Lock, Daniel L. Rubin |
| 2017 | AMIA | Intelligent Word Embeddings of Free-Text Radiology Reports. | Imon Banerjee, Sriraman Madhavan, Roger Eric Goldman, Daniel L. Rubin |
| 2017 | AMIA | Toward Automated Pre-Biopsy Thyroid Cancer Risk Estimation in Ultrasound. | Alfiia Galimzianova, Sean M. Siebert, Aya Kamaya, Terry S. Desser, Daniel L. Rubin |
| 2017 | AMIA | Differential Data Augmentation Techniques for Medical Imaging Classification Tasks. | Zeshan Hussain, Francisco Gimenez, Darvin Yi, Daniel L. Rubin |
| 2017 | AMIA | The LOINC/RSNA Radiology Playbook: A unified terminology for radiology procedures. | Daniel J. Vreeman, Ken Wang, Chris Carr, Beverly Collins, Swapna Abhyankar, Jamalynne Deckard, Clement J. McDonald, Daniel L. Rubin, Curtis P. Langlotz |
| 2017 | AMIA | Predicting Non-Small Cell Lung Cancer Diagnosis and Prognosis by Fully Automated Microscopic Pathology Image Features. | Kun-Hsing Yu, Ce Zhang, Gerald J. Berry, Russ B. Altman, Christopher R, Daniel L. Rubin, Michael Snyder |
| 2015 | AMIA | Automated Grading of Gliomas using Deep Learning in Digital Pathology Images: A modular approach with ensemble of convolutional neural networks. | Mehmet Gnhan Ertosun, Daniel L. Rubin |
| 2015 | AMIA | ePAD: Leveraging image data in learning healthcare systems. | Daniel L. Rubin |
| 2015 | AMIA | Polychromatic X-Ray Absorptiometry to Quantify Breast Density Volume, Ratio and their Associated Breast Cancer Risk in Full-Digital Mammography. | Luis de Sisternes, Joseph Rothstein, Abra Jeffers, Weiva Sieh, Daniel L. Rubin |
| 2015 | CBMS | Automatic Classification of Cancer Tumors Using Image Annotations and Ontologies. | Edson F. Luque, Daniel L. Rubin, Dilvan A. Moreira |
| 2015 | CBMS | 3D Markup of Radiological Images in ePAD, a Web-Based Image Annotation Tool. | Dilvan A. Moreira, Cleber Hage, Edson F. Luque, Debra Willrett, Daniel L. Rubin |
| 2015 | ECIR | Semantic Retrieval of Radiological Images with Relevance Feedback. | Camille Kurtz, Paul-Andr Idoux, Avinash Thangali, Florence Cloppet, Christopher F. Beaulieu, Daniel L. Rubin |
| 2014 | AMIA | A Novel Method to Assess Incompleteness of Mammography Report Content. | Francisco Gimenez, Yirong Wu, Elizabeth S. Burnside, Daniel L. Rubin |
| 2014 | ICIP | A semantic framework for the retrieval of similar radiological images based on medical annotations. | Camille Kurtz, Adrien Depeursinge, Christopher F. Beaulieu, Daniel L. Rubin |
| 2013 | AMIA | Classifying Benign and Malignant Lung Diseases by Applying Machine Learning Methods to Microscopic Pathology Images. | Kun-Hsing Yu, Shanshan Tuo, Daniel L. Rubin |
| 2012 | AMIA | Automatic Annotation of Radiological Observations in Liver CT Images. | Francisco Gimenez, Jiajing Xu, Yi Liu, Tiffany Ting Liu, Christopher F. Beaulieu, Daniel L. Rubin, Sandy Napel |
| 2012 | WETICE | Using the Semantic Web and Web Apps to Connect Radiologists and Oncologists. | Kleberson J. A. Serique, Alan Snyder, Debra Willrett, Daniel L. Rubin, Dilvan A. Moreira |
| 2009 | AMIA | Semantic Reasoning with Image Annotations for Tumor Assessment. | Mia A. Levy, Martin J. O'Connor, Daniel L. Rubin |
| 2008 | AMIA | Tool Support to Enable Evaluation of the Clinical Response to Treatment. | Mia A. Levy, Daniel L. Rubin |
| 2008 | AMIA | A Bayesian Classifier for Differentiating Benign versus Malignant Thyroid Nodules using Sonographic Features. | Yueyi I. Liu, Aya Kamaya, Terry S. Desser, Daniel L. Rubin |
| 2008 | AMIA | FMA-RadLex: An Application Ontology of Radiological Anatomy derived from the Foundational Model of Anatomy Reference Ontology. | Jos L. V. Mejino Jr., Daniel L. Rubin, James F. Brinkley |
| 2008 | AMIA | Untitled record | Daniel L. Rubin, Cesar Rodriguez, Priyanka Shah, Christopher F. Beaulieu |
| 2007 | AMIA | LesionViewer: A Tool for Tracking Cancer Lesions Over Time. | Mia A. Levy, Ankit Garg, Aaron Tam, Yael Garten, Daniel L. Rubin |
| 2006 | AMIA | Ontology-Based Representation of Simulation Models of Physiology. | Daniel L. Rubin, David Grossman, Maxwell Lewis Neal, Daniel L. Cook, James B. Bassingthwaighte, Mark A. Musen |
| 2006 | AMIA | Ontology-based Annotation and Query of Tissue Microarray Data. | Nigam H. Shah, Daniel L. Rubin, Kaustubh S. Supekar, Mark A. Musen |
| 2005 | AMIA | Challenges in Converting Frame-Based Ontology into OWL: the Foundational Model of Anatomy Case-Study. | Olivier Dameron, Daniel L. Rubin, Mark A. Musen |
| 2005 | AMIA | Use of Description Logic Classification to Reason about Consequences of Penetrating Injuries. | Daniel L. Rubin, Olivier Dameron, Mark A. Musen |
| 2005 | AMIA | Protg-OWL: Creating Ontology-Driven Reasoning Applications with the Web Ontology Language. | Daniel L. Rubin, Holger Knublauch, Ray W. Fergerson, Olivier Dameron, Mark A. Musen |
| 2002 | ISMB | Representing genetic sequence data for pharmacogenomics: an evolutionary approach using ontological and relational models. | Daniel L. Rubin, Farhad Shafa, Diane E. Oliver, Micheal Hewett, Russ B. Altman |
| 2002 | PSB | Ontology Development for a Pharmacogenetics Knowledge Base. | Diane E. Oliver, Daniel L. Rubin, Joshua M. Stuart, Micheal Hewett, Teri E. Klein, Russ B. Altman |
| 2002 | PSB | Automating Data Acquisition into Ontologies from Pharmacogenetics Relational Data Sources Using Declarative Object Definitions and XML. | Daniel L. Rubin, Micheal Hewett, Diane E. Oliver, Teri E. Klein, Russ B. Altman |
| 2000 | AMIA | A Bayesian network for mammography. | Elizabeth S. Burnside, Daniel L. Rubin, Ross D. Shachter |
| 2000 | AMIA | Knowledge representation and tool support for critiquing clinical trial protocols. | Daniel L. Rubin, John H. Gennari, Mark A. Musen |
| 1999 | AMIA | Tool support for authoring eligibility criteria for cancer trials. | Daniel L. Rubin, John H. Gennari, Sandra Srinivas, Allen Yuen, Herbert Kaizer, Mark A. Musen, John S. Silva |