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ORCIDDeepta Rajan
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2020 – today
- 2023
[c19]Vivek Sivaraman Narayanaswamy, Yamen Mubarka, Rushil Anirudh, Deepta Rajan, Jayaraman J. Thiagarajan:
Exploring Inlier and Outlier Specification for Improved Medical OOD Detection. ICCV (Workshops) 2023: 4591-4600- [c18]Vivek Sivaraman Narayanaswamy, Yamen Mubarka, Rushil Anirudh, Deepta Rajan, Andreas Spanias, Jayaraman J. Thiagarajan:
Know Your Space: Inlier and Outlier Construction for Calibrating Medical OOD Detectors. MIDL 2023: 190-211 - 2022
- [i15]Vivek Sivaraman Narayanaswamy, Yamen Mubarka, Rushil Anirudh, Deepta Rajan, Andreas Spanias, Jayaraman J. Thiagarajan:
Revisiting Inlier and Outlier Specification for Improved Out-of-Distribution Detection. CoRR abs/2207.05286 (2022) - [i14]Jin Li, Deepta Rajan, Chintan Shah, Dinkar Juyal, Shreya Chakraborty, Chandan Akiti, Filip Kos, Janani Iyer, Anand Sampat, Ali Behrooz:
Self-training of Machine Learning Models for Liver Histopathology: Generalization under Clinical Shifts. CoRR abs/2211.07692 (2022) - 2021
- [j3]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:
On the role of artificial intelligence in medical imaging of COVID-19. Patterns 2(6): 100269 (2021) - [j2]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:
On the role of artificial intelligence in medical imaging of COVID-19. Patterns 2(8): 100330 (2021) - [c17]Joshua K. Lee, Yuheng Bu, Deepta Rajan, Prasanna Sattigeri, Rameswar Panda, Subhro Das, Gregory W. Wornell:
Fair Selective Classification Via Sufficiency. ICML 2021: 6076-6086 - [c16]Deepta Rajan, Jayaraman J. Thiagarajan, Alexandros Karargyris, Satyananda Kashyap:
Self-training with improved regularization for sample-efficient chest x-ray classification. Medical Imaging: Computer-Aided Diagnosis 2021 - [c15]Yiting Xie, Deepta Rajan, Larissa C. Schudlo, Yusuke Takeuchi, Benedikt Graf, Adam Coy, Mohammadreza Negahdar, Vandana V. Mukherjee, David Beymer, Arun Krishnan:
Automatic localization of lung opacity in chest CT images: a real-world study. Medical Imaging: Computer-Aided Diagnosis 2021 - [c14]Jayaraman J. Thiagarajan, Vivek Sivaraman Narayanaswamy, Deepta Rajan, Jia Liang, Akshay Chaudhari, Andreas Spanias:
Designing Counterfactual Generators using Deep Model Inversion. NeurIPS 2021: 16873-16884 - [i13]Vivek Sivaraman Narayanaswamy, Jayaraman J. Thiagarajan, Deepta Rajan, Andreas Spanias:
Loss Estimators Improve Model Generalization. CoRR abs/2103.03788 (2021) - [i12]Jayaraman J. Thiagarajan, Vivek Sivaraman Narayanaswamy, Deepta Rajan, Jason Liang, Akshay Chaudhari, Andreas Spanias:
Designing Counterfactual Generators using Deep Model Inversion. CoRR abs/2109.14274 (2021) - 2020
- [c13]Jayaraman J. Thiagarajan, Bindya Venkatesh, Deepta Rajan:
Learn-By-Calibrating: Using Calibration As A Training Objective. ICASSP 2020: 3632-3636 - [c12]Jayaraman J. Thiagarajan, Bindya Venkatesh, Deepta Rajan, Prasanna Sattigeri:
Improving Reliability of Clinical Models Using Prediction Calibration. UNSURE/GRAIL@MICCAI 2020: 71-80 - [c11]Luyao Shi, Deepta Rajan, Shafiq Abedin, Manikanta Srikar Yellapragada, David Beymer, Ehsan Dehghan:
Automatic Diagnosis of Pulmonary Embolism Using an Attention-guided Framework: A Large-scale Study. MIDL 2020: 743-754 - [i11]Jayaraman J. Thiagarajan, Prasanna Sattigeri, Deepta Rajan, Bindya Venkatesh:
Calibrating Healthcare AI: Towards Reliable and Interpretable Deep Predictive Models. CoRR abs/2004.14480 (2020) - [i10]Deepta Rajan, Jayaraman J. Thiagarajan, Alexandros Karargyris, Satyananda Kashyap:
Self-Training with Improved Regularization for Few-Shot Chest X-Ray Classification. CoRR abs/2005.02231 (2020) - [i9]Luyao Shi, Deepta Rajan, Shafiq Abedin, Manikanta Srikar Yellapragada, David Beymer, Ehsan Dehghan:
Automatic Diagnosis of Pulmonary Embolism Using an Attention-guided Framework: A Large-scale Study. CoRR abs/2006.00074 (2020)
2010 – 2019
- 2019
- [c10]Tomasz Kornuta, Deepta Rajan, Chaitanya Shivade, Alexis Asseman, Ahmet S. Ozcan:
Leveraging Medical Visual Question Answering with Supporting Facts. CLEF (Working Notes) 2019 - [c9]Deepta Rajan, David Beymer, Shafiqul Abedin, Ehsan Dehghan:
Pi-PE: A Pipeline for Pulmonary Embolism Detection using Sparsely Annotated 3D CT Images. ML4H@NeurIPS 2019: 220-232 - [i8]Deepta Rajan, David Beymer, Girish Narayan:
Generalization Studies of Neural Network Models for Cardiac Disease Detection Using Limited Channel ECG. CoRR abs/1901.03295 (2019) - [i7]Tomasz Kornuta, Deepta Rajan, Chaitanya Shivade, Alexis Asseman, Ahmet S. Ozcan:
Leveraging Medical Visual Question Answering with Supporting Facts. CoRR abs/1905.12008 (2019) - [i6]Deepta Rajan, David Beymer, Shafiqul Abedin, Ehsan Dehghan:
Pi-PE: A Pipeline for Pulmonary Embolism Detection using Sparsely Annotated 3D CT Images. CoRR abs/1910.02175 (2019) - [i5]Jayaraman J. Thiagarajan, Bindya Venkatesh, Deepta Rajan:
Learn-By-Calibrating: Using Calibration as a Training Objective. CoRR abs/1910.14175 (2019) - 2018
- [c8]Huan Song, Deepta Rajan, Jayaraman J. Thiagarajan, Andreas Spanias:
Attend and Diagnose: Clinical Time Series Analysis Using Attention Models. AAAI 2018: 4091-4098 - [c7]Deepta Rajan, David Beymer, Girish Narayan:
Improving Generalization of Deep Models for Cardiac Disease Detection Using Limited Channel ECG. CinC 2018: 1-4 - [c6]Deepta Rajan, Jayaraman J. Thiagarajan:
A Generative Modeling Approach to Limited Channel ECG Classification. EMBC 2018: 2571-2574 - [i4]Deepta Rajan, Jayaraman J. Thiagarajan:
A Generative Modeling Approach to Limited Channel ECG Classification. CoRR abs/1802.06458 (2018) - [i3]Jayaraman J. Thiagarajan, Deepta Rajan, Prasanna Sattigeri:
Can Deep Clinical Models Handle Real-World Domain Shifts? CoRR abs/1809.07806 (2018) - 2017
- [i2]Huan Song, Deepta Rajan, Jayaraman J. Thiagarajan, Andreas Spanias:
Attend and Diagnose: Clinical Time Series Analysis using Attention Models. CoRR abs/1711.03905 (2017) - 2016
- [c5]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:
Medical sieve: a cognitive assistant for radiologists and cardiologists. Medical Imaging: Computer-Aided Diagnosis 2016: 97850A - [c4]Tanveer F. Syeda-Mahmood, Yanrong Guo, Mehdi Moradi, David Beymer, Deepta Rajan, Yu Cao, Yaniv Gur, Mohammadreza Negahdar:
Identifying Patients at Risk for Aortic Stenosis Through Learning from Multimodal Data. MICCAI (3) 2016: 238-245 - 2014
- [j1]Jayaraman J. Thiagarajan, Karthikeyan Natesan Ramamurthy, Deepta Rajan, Andreas Spanias, Anup Puri, David H. Frakes:
Kernel Sparse Models for Automated Tumor Segmentation. Int. J. Artif. Intell. Tools 23(3) (2014) - [c3]Mahesh K. Banavar, Deepta Rajan, Andrew Strom, Photini Spanias, Xue Sophia Zhang, Henry Braun, Andreas Spanias:
Embedding Android signal processing apps in a high school math class - An RET project. FIE 2014: 1-4 - 2013
- [c2]Deepta Rajan, Andreas Spanias, Suhas Ranganath, Mahesh K. Banavar, Photini Spanias:
Health monitoring laboratories by interfacing physiological sensors to mobile android devices. FIE 2013: 1049-1055 - [i1]Jayaraman J. Thiagarajan, Karthikeyan Natesan Ramamurthy, Deepta Rajan, Anup Puri, David H. Frakes, Andreas Spanias:
Kernel Sparse Models for Automated Tumor Segmentation. CoRR abs/1303.2610 (2013) - 2012
- [c1]Jayaraman J. Thiagarajan, Deepta Rajan, Karthikeyan Natesan Ramamurthy, David H. Frakes, Andreas Spanias:
Automated tumor segmentation using kernel sparse representations. BIBE 2012: 401-406
Coauthor Index
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