Automated tumor segmentation using kernel sparse representations. - Jayaraman J. Thiagarajan; Deepta Rajan; Karthikeyan Natesan Ramamurthy; David H. Frakes; Andreas Spanias (BIBE, 2012)

Health monitoring laboratories by interfacing physiological sensors to mobile android devices. - Deepta Rajan; Andreas Spanias; Suhas Ranganath; Mahesh K. Banavar; Photini Spanias (FIE, 2013)

Embedding Android signal processing apps in a high school math class - An RET project. - Mahesh K. Banavar; Deepta Rajan; Andrew Strom; Photini Spanias; Xue Sophia Zhang; Henry Braun; Andreas Spanias (FIE, 2014)

Kernel Sparse Models for Automated Tumor Segmentation. - Jayaraman J. Thiagarajan; Karthikeyan Natesan Ramamurthy; Deepta Rajan; Andreas Spanias; Anup Puri; David H. Frakes (Int. J. Artif. Intell. Tools, 2014)

Medical sieve: a cognitive assistant for radiologists and cardiologists. - Tanveer F. Syeda-Mahmood; Eugene Walach; David Beymer; Flora Gilboa-Solomon; Mehdi Moradi; Pavel Kisilev; Deepika Kakrania; Colin B. Compas; Hongzhi Wang 002; Mohammadreza Negahdar; Yu Cao; Tyler Baldwin; et al. (Computer-Aided Diagnosis, 2016)

Identifying Patients at Risk for Aortic Stenosis Through Learning from Multimodal Data. - Tanveer F. Syeda-Mahmood; Yanrong Guo; Mehdi Moradi; David Beymer; Deepta Rajan; Yu Cao; Yaniv Gur; Mohammadreza Negahdar (MICCAI (3), 2016)

Attend and Diagnose: Clinical Time Series Analysis Using Attention Models. - Huan Song; Deepta Rajan; Jayaraman J. Thiagarajan; Andreas Spanias (AAAI, 2018)

Improving Generalization of Deep Models for Cardiac Disease Detection Using Limited Channel ECG. - Deepta Rajan; David Beymer; Girish Narayan (CinC, 2018)

A Generative Modeling Approach to Limited Channel ECG Classification. - Deepta Rajan; Jayaraman J. Thiagarajan (EMBC, 2018)

Can Deep Clinical Models Handle Real-World Domain Shifts? - Jayaraman J. Thiagarajan; Deepta Rajan; Prasanna Sattigeri (CoRR, 2018)

Leveraging Medical Visual Question Answering with Supporting Facts. - Tomasz Kornuta; Deepta Rajan; Chaitanya Shivade; Alexis Asseman; Ahmet S. Ozcan (CLEF (Working Notes), 2019)

Pi-PE: A Pipeline for Pulmonary Embolism Detection using Sparsely Annotated 3D CT Images. - Deepta Rajan; David Beymer; Shafiqul Abedin; Ehsan Dehghan (ML4H@NeurIPS, 2019)

Generalization Studies of Neural Network Models for Cardiac Disease Detection Using Limited Channel ECG. - Deepta Rajan; David Beymer; Girish Narayan (CoRR, 2019)

Learn-By-Calibrating: Using Calibration As A Training Objective. - Jayaraman J. Thiagarajan; Bindya Venkatesh; Deepta Rajan (ICASSP, 2020)

Improving Reliability of Clinical Models Using Prediction Calibration. - Jayaraman J. Thiagarajan; Bindya Venkatesh; Deepta Rajan; Prasanna Sattigeri (UNSURE/GRAIL@MICCAI, 2020)

Automatic Diagnosis of Pulmonary Embolism Using an Attention-guided Framework: A Large-scale Study. - Luyao Shi; Deepta Rajan; Shafiq Abedin; Manikanta Srikar Yellapragada; David Beymer; Ehsan Dehghan (MIDL, 2020)

Calibrating Healthcare AI: Towards Reliable and Interpretable Deep Predictive Models. - Jayaraman J. Thiagarajan; Prasanna Sattigeri; Deepta Rajan; Bindya Venkatesh (CoRR, 2020)

Self-Training with Improved Regularization for Few-Shot Chest X-Ray Classification. - Deepta Rajan; Jayaraman J. Thiagarajan; Alexandros Karargyris; Satyananda Kashyap (CoRR, 2020)

Fair Selective Classification Via Sufficiency. - Joshua K. Lee; Yuheng Bu; Deepta Rajan; Prasanna Sattigeri; Rameswar Panda; Subhro Das; Gregory W. Wornell (ICML, 2021)

Self-training with improved regularization for sample-efficient chest x-ray classification. - Deepta Rajan; Jayaraman J. Thiagarajan; Alexandros Karargyris; Satyananda Kashyap (Computer-Aided Diagnosis, 2021)

Automatic localization of lung opacity in chest CT images: a real-world study. - Yiting Xie; Deepta Rajan; Larissa C. Schudlo; Yusuke Takeuchi; Benedikt Graf; Adam Coy; Mohammadreza Negahdar; Vandana V. Mukherjee; David Beymer; Arun Krishnan (Computer-Aided Diagnosis, 2021)

Designing Counterfactual Generators using Deep Model Inversion. - Jayaraman J. Thiagarajan; Vivek Sivaraman Narayanaswamy; Deepta Rajan; Jason Liang; Akshay Chaudhari; Andreas Spanias (NeurIPS, 2021)

Loss Estimators Improve Model Generalization. - Vivek Sivaraman Narayanaswamy; Jayaraman J. Thiagarajan; Deepta Rajan; Andreas Spanias (CoRR, 2021)

On the role of artificial intelligence in medical imaging of COVID-19. - Jannis Born; David Beymer; Deepta Rajan; Adam Coy; Vandana V. Mukherjee; Matteo Manica; Prasanth Prasanna; Deddeh Ballah; Michal Guindy; Dorith Shaham; Pallav L. Shah; Emmanouil Karteris; et al. (Patterns, 2021)

Revisiting Inlier and Outlier Specification for Improved Out-of-Distribution Detection. - Vivek Sivaraman Narayanaswamy; Yamen Mubarka; Rushil Anirudh; Deepta Rajan; Andreas Spanias; Jayaraman J. Thiagarajan (CoRR, 2022)

Self-training of Machine Learning Models for Liver Histopathology: Generalization under Clinical Shifts. - Jin Li; Deepta Rajan; Chintan Shah; Dinkar Juyal; Shreya Chakraborty; Chandan Akiti; Filip Kos; Janani Iyer; Anand Sampat; Ali Behrooz (CoRR, 2022)

Exploring Inlier and Outlier Specification for Improved Medical OOD Detection. - Vivek Sivaraman Narayanaswamy; Yamen Mubarka; Rushil Anirudh; Deepta Rajan; Jayaraman J. Thiagarajan (ICCV (Workshops), 2023)

Know Your Space: Inlier and Outlier Construction for Calibrating Medical OOD Detectors. - Vivek Sivaraman Narayanaswamy; Yamen Mubarka; Rushil Anirudh; Deepta Rajan; Andreas Spanias; Jayaraman J. Thiagarajan (MIDL, 2023)