LT-ViT: A Vision Transformer for multi-label Chest X-ray classification

• URL: http://arxiv.org/abs/2311.07263v1
• Date: Mon, 13 Nov 2023 12:02:46 GMT
• Title: LT-ViT: A Vision Transformer for multi-label Chest X-ray classification
• Authors: Umar Marikkar and Sara Atito and Muhammad Awais and Adam Mahdi
• Abstract summary: Vision Transformers (ViTs) are widely adopted in medical imaging tasks, and some existing efforts have been directed towards vision-language training for Chest X-rays (CXRs) We have developed LT-ViT, a transformer that utilizes combined attention between image tokens and randomly auxiliary tokens that represent labels.
• Score: 2.3022732986382213
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision Transformers (ViTs) are widely adopted in medical imaging tasks, and some existing efforts have been directed towards vision-language training for Chest X-rays (CXRs). However, we envision that there still exists a potential for improvement in vision-only training for CXRs using ViTs, by aggregating information from multiple scales, which has been proven beneficial for non-transformer networks. Hence, we have developed LT-ViT, a transformer that utilizes combined attention between image tokens and randomly initialized auxiliary tokens that represent labels. Our experiments demonstrate that LT-ViT (1) surpasses the state-of-the-art performance using pure ViTs on two publicly available CXR datasets, (2) is generalizable to other pre-training methods and therefore is agnostic to model initialization, and (3) enables model interpretability without grad-cam and its variants.

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