44 citations · 101 across the 12 of their papers we have counts for
15 papers
Self-training of Machine Learning Models for Liver Histopathology: Generalization under Clinical Shifts
Jin Li, Deepta Rajan, Chintan Shah +7
Histopathology images are gigapixel-sized and include features and information at different resolutions. Collecting annotations in histopathology requires highly specialized pathol…
Know Your Space: Inlier and Outlier Construction for Calibrating Medical OOD Detectors
Vivek Narayanaswamy, Yamen Mubarka, Rushil Anirudh +3
We focus on the problem of producing well-calibrated out-of-distribution (OOD) detectors, in order to enable safe deployment of medical image classifiers. Motivated by the difficul…
Designing Counterfactual Generators using Deep Model Inversion
Jayaraman J. Thiagarajan, Vivek Narayanaswamy, Deepta Rajan +3
Explanation techniques that synthesize small, interpretable changes to a given image while producing desired changes in the model prediction have become popular for introspecting b…
Loss Estimators Improve Model Generalization
Vivek Narayanaswamy, Jayaraman J. Thiagarajan, Deepta Rajan +1
With increased interest in adopting AI methods for clinical diagnosis, a vital step towards safe deployment of such tools is to ensure that the models not only produce accurate pre…
Automatic Diagnosis of Pulmonary Embolism Using an Attention-guided Framework: A Large-scale Study
Luyao Shi, Deepta Rajan, Shafiq Abedin +3
Pulmonary Embolism (PE) is a life-threatening disorder associated with high mortality and morbidity. Prompt diagnosis and immediate initiation of therapeutic action is important. W…
Self-Training with Improved Regularization for Sample-Efficient Chest X-Ray Classification
Deepta Rajan, Jayaraman J. Thiagarajan, Alexandros Karargyris +1
Automated diagnostic assistants in healthcare necessitate accurate AI models that can be trained with limited labeled data, can cope with severe class imbalances and can support si…