Deepta Rajan

According to our database, Deepta Rajan authored at least 29 papers between 2012 and 2023.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of four.

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

Proceedings of the Medical Imaging with Deep Learning, 2023

Exploring Inlier and Outlier Specification for Improved Medical OOD Detection.

Vivek Sivaraman Narayanaswamy, Yamen Mubarka, Rushil Anirudh, Deepta Rajan, Jayaraman J. Thiagarajan

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

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

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

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, Jan L. Robertus, Maria Gabrani, Michal Rosen-Zvi

Patterns, 2021

Designing Counterfactual Generators using Deep Model Inversion.

Jayaraman J. Thiagarajan, Vivek Sivaraman Narayanaswamy, Deepta Rajan, Jason Liang, Akshay Chaudhari, Andreas Spanias

CoRR, 2021

Loss Estimators Improve Model Generalization.

Vivek Sivaraman Narayanaswamy, Jayaraman J. Thiagarajan, Deepta Rajan, Andreas Spanias

CoRR, 2021

Designing Counterfactual Generators using Deep Model Inversion.

Jayaraman J. Thiagarajan, Vivek Sivaraman Narayanaswamy, Deepta Rajan, Jia Liang, Akshay Chaudhari, Andreas Spanias

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 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

Proceedings of the Medical Imaging 2021: Computer-Aided Diagnosis, 2021

Self-training with improved regularization for sample-efficient chest x-ray classification.

Deepta Rajan, Jayaraman J. Thiagarajan, Alexandros Karargyris, Satyananda Kashyap

Proceedings of the Medical Imaging 2021: Computer-Aided Diagnosis, 2021

Fair Selective Classification Via Sufficiency.

Joshua K. Lee, Yuheng Bu, Deepta Rajan, Prasanna Sattigeri, Rameswar Panda, Subhro Das, Gregory W. Wornell

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Self-Training with Improved Regularization for Few-Shot Chest X-Ray Classification.

Deepta Rajan, Jayaraman J. Thiagarajan, Alexandros Karargyris, Satyananda Kashyap

CoRR, 2020

Calibrating Healthcare AI: Towards Reliable and Interpretable Deep Predictive Models.

Jayaraman J. Thiagarajan, Prasanna Sattigeri, Deepta Rajan, Bindya Venkatesh

CoRR, 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

Proceedings of the International Conference on Medical Imaging with Deep Learning, 2020

Improving Reliability of Clinical Models Using Prediction Calibration.

Jayaraman J. Thiagarajan, Bindya Venkatesh, Deepta Rajan, Prasanna Sattigeri

Proceedings of the Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis, 2020

Learn-By-Calibrating: Using Calibration As A Training Objective.

Jayaraman J. Thiagarajan, Bindya Venkatesh, Deepta Rajan

Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

2019

Generalization Studies of Neural Network Models for Cardiac Disease Detection Using Limited Channel ECG.

Deepta Rajan, David Beymer, Girish Narayan

CoRR, 2019

Pi-PE: A Pipeline for Pulmonary Embolism Detection using Sparsely Annotated 3D CT Images.

Deepta Rajan, David Beymer, Shafiqul Abedin, Ehsan Dehghan

Proceedings of the Machine Learning for Health Workshop, 2019

Leveraging Medical Visual Question Answering with Supporting Facts.

Tomasz Kornuta, Deepta Rajan, Chaitanya Shivade, Alexis Asseman, Ahmet S. Ozcan

Proceedings of the Working Notes of CLEF 2019, 2019

2018

Can Deep Clinical Models Handle Real-World Domain Shifts?

Jayaraman J. Thiagarajan, Deepta Rajan, Prasanna Sattigeri

CoRR, 2018

A Generative Modeling Approach to Limited Channel ECG Classification.

Deepta Rajan, Jayaraman J. Thiagarajan

Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2018

Improving Generalization of Deep Models for Cardiac Disease Detection Using Limited Channel ECG.

Deepta Rajan, David Beymer, Girish Narayan

Proceedings of the Computing in Cardiology, 2018

Attend and Diagnose: Clinical Time Series Analysis Using Attention Models.

Huan Song, Deepta Rajan, Jayaraman J. Thiagarajan, Andreas Spanias

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

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

Proceedings of the Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016, 2016

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, Mohammadreza Negahdar, Yu Cao, Tyler Baldwin, Yufan Guo, Yaniv Gur, Deepta Rajan, Aviad Zlotnick, Simona Rabinovici-Cohen, Rami Ben-Ari, Guy Amit, Prasanth Prasanna, J. Morey, Orest B. Boyko, Sharbell Y. Hashoul

Proceedings of the Medical Imaging 2016: Computer-Aided Diagnosis, San Diego, California, United States, 27 February, 2016

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

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

Proceedings of the IEEE Frontiers in Education Conference, 2014

2013

Health monitoring laboratories by interfacing physiological sensors to mobile android devices.

Deepta Rajan, Andreas Spanias, Suhas Ranganath, Mahesh K. Banavar, Photini Spanias

Proceedings of the IEEE Frontiers in Education Conference, 2013

2012

Automated tumor segmentation using kernel sparse representations.

Jayaraman J. Thiagarajan, Deepta Rajan, Karthikeyan Natesan Ramamurthy, David H. Frakes, Andreas Spanias

Proceedings of the 12th IEEE International Conference on Bioinformatics & Bioengineering, 2012