13 citations · 18 across the 5 of their papers we have counts for
5 papers
Exploring Image Augmentations for Siamese Representation Learning with Chest X-Rays
Rogier van der Sluijs, Nandita Bhaskhar, Daniel Rubin +2
Image augmentations are quintessential for effective visual representation learning across self-supervised learning techniques. While augmentation strategies for natural imaging ha…
Data-Limited Tissue Segmentation using Inpainting-Based Self-Supervised Learning
Jeffrey Dominic, Nandita Bhaskhar, Arjun D. Desai +8
Although supervised learning has enabled high performance for image segmentation, it requires a large amount of labeled training data, which can be difficult to obtain in the medic…
TRUST-LAPSE: An Explainable and Actionable Mistrust Scoring Framework for Model Monitoring
Nandita Bhaskhar, Daniel L. Rubin, Christopher Lee-Messer
Continuous monitoring of trained ML models to determine when their predictions should and should not be trusted is essential for their safe deployment. Such a framework ought to be…
Double Descent Optimization Pattern and Aliasing: Caveats of Noisy Labels
Florian Dubost, Erin Hong, Max Pike +5
Optimization plays a key role in the training of deep neural networks. Deciding when to stop training can have a substantial impact on the performance of the network during inferen…
Semi-Supervised Learning for Sparsely-Labeled Sequential Data: Application to Healthcare Video Processing
Florian Dubost, Erin Hong, Nandita Bhaskhar +3
Labeled data is a critical resource for training and evaluating machine learning models. However, many real-life datasets are only partially labeled. We propose a semi-supervised m…