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Using LLMs in Software Requirements Specifications: An Empirical Evaluation
Hence, we conclude that the LLMs can be gainfully used by software engineers to increase productivity by saving time and effort in generating, validating and rectifying software requirements.
Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge
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 relatively rare conditions.
Generalized Cross-domain Multi-label Few-shot Learning for Chest X-rays
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 derived from different domains; and (iii) classes that appear during training may have partial overlap with classes of interest during evaluation.
How Can We Tame the Long-Tail of Chest X-ray Datasets?
While it is hard to define an exhaustive list of these abnormalities, which may co-occur on a chest X-ray, few of them are quite commonly observed and are abundantly represented in CXR datasets used to train deep learning models for automated inference.
Can we Adopt Self-supervised Pretraining for Chest X-Rays?
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.
