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20222024
most citedHow Can We Tame the Long-Tail of Chest X-ray Datasets?

3 citations · 5 across the 3 of their papers we have counts for

collaborators

5 papers

cs.SE2024

Using LLMs in Software Requirements Specifications: An Empirical Evaluation

Madhava Krishna, Bhagesh Gaur, Arsh Verma +1

The creation of a Software Requirements Specification (SRS) document is important for any software development project. Given the recent prowess of Large Language Models (LLMs) in…

cs.CV2023

Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge

Gregory Holste, Yiliang Zhou, Song Wang +22

Many real-world image recognition problems, such as diagnostic medical imaging exams, are "long-tailed" $\unicode{x2013}$ there are a few common findings followed by many more rela…

cs.CV20231 cited

Generalized Cross-domain Multi-label Few-shot Learning for Chest X-rays

Aroof Aimen, Arsh Verma, Makarand Tapaswi +1

Real-world application of chest X-ray abnormality classification requires dealing with several challenges: (i) limited training data; (ii) training and evaluation sets that are der…

eess.IV20233 cited

How Can We Tame the Long-Tail of Chest X-ray Datasets?

Arsh Verma

Chest X-rays (CXRs) are a medical imaging modality that is used to infer a large number of abnormalities. While it is hard to define an exhaustive list of these abnormalities, whic…

cs.CV20221 cited

Can we Adopt Self-supervised Pretraining for Chest X-Rays?

Arsh Verma, Makarand Tapaswi

Chest radiograph (or Chest X-Ray, CXR) is a popular medical imaging modality that is used by radiologists across the world to diagnose heart or lung conditions. Over the last decad…