mlsanityData to 14 Oct 2026

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

arXiv 2311.07263 ↗First posted 13 Nov 2023cs.CV cs.LGcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 22 of its 27 references that are in the map. First posted on arXiv 13 Nov 2023.

Builds on

22 of 27 references.

References · 22

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10 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 2.

3 papers.

  1. 2024Mask of truth: model sensitivity to unexpected regions of medical imagescited by 6
  2. 2026From pre-training to downstream performance: Does domain-specific pre-training make sense?cited by 1
  3. 2026Integrating Unimodal and Vision-Language Representations in Latent Space for Multi-Label Chest X-Ray Classificationcited by 0