mlsanityData to 7 Oct 2026

Artificially Generated Visual Scanpath Improves Multi-label Thoracic Disease Classification in Chest X-Ray Images

arXiv 2503.00657 ↗First posted 1 Mar 2025eess.IV cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

Expert radiologists visually scan Chest X-Ray (CXR) images, sequentially fixating on anatomical structures to perform disease diagnosis. An automatic multi-label classifier of diseases in CXR images can benefit by incorporating aspects of the radiologists' approach. Recorded visual scanpaths of radiologists on CXR images can be used for the said purpose. But, such scanpaths are not available for most CXR images, which creates a gap even for modern deep learning based classifiers. This paper proposes to mitigate this gap by generating effective artificial visual scanpaths using a visual scanpath prediction model for CXR images. Further, a multi-class multi-label classifier framework is proposed that uses a generated scanpath and visual image features to classify diseases in CXR images. While the scanpath predictor is based on a recurrent neural network, the multi-label classifier involves a novel iterative sequential model with an attention module. We show that our scanpath predictor generates human-like visual scanpaths. We also demonstrate that the use of artificial visual scanpaths improves multi-class multi-label disease classification results on CXR images. The above observations are made from experiments involving around 0.2 million CXR images from 2 widely-used datasets considering the multi-label classification of 14 pathological findings. Code link: https://github.com/ashishverma03/SDC

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 27 of its 73 references that are in the map. First posted on arXiv 1 Mar 2025.

Builds on

27 of 73 references.

References · 27

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2016Densely Connected Convolutional Networkscited by 6,691
  4. 2019CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparisoncited by 1,410
  5. 2017ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseasescited by 969
  6. 2019MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographscited by 590
  7. 2017CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learningcited by 459
  8. 2017On human motion prediction using recurrent neural networkscited by 336
  9. 2021Big Self-Supervised Models Advance Medical Image Classificationcited by 320
  10. 2021Deep Learning for Chest X-ray Analysis: A Surveycited by 84
  11. 2021XProtoNet: Diagnosis in Chest Radiography with Global and Local Explanationscited by 72
  12. 2018Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classificationcited by 57
15 more
  1. 2017Thoracic Disease Identification and Localization with Limited Supervisioncited by 55
  2. 2020Creation and Validation of a Chest X-Ray Dataset with Eye-tracking and Report Dictation for AI Developmentcited by 55
  3. 2021Weakly Supervised Contrastive Learning for Chest X-Ray Report Generationcited by 39
  4. 2013Actions in the Eye: Dynamic Gaze Datasets and Learnt Saliency Models for Visual Recognitioncited by 36
  5. 2021REFLACX, a dataset of reports and eye-tracking data for localization of abnormalities in chest x-rayscited by 34
  6. 2021ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image Classificationcited by 34
  7. 2021Learning Hierarchical Attention for Weakly-supervised Chest X-Ray Abnormality Localization and Diagnosiscited by 24
  8. 2019ImageGCN: Multi-Relational Image Graph Convolutional Networks for Disease Identification with Chest X-rayscited by 22
  9. 2021Interpretability-Driven Sample Selection Using Self Supervised Learning For Disease Classification And Segmentationcited by 22
  10. 2018Weakly Supervised Deep Learning for Thoracic Disease Classification and Localization on Chest X-rayscited by 21
  11. 2021AnaXNet: Anatomy Aware Multi-label Finding Classification in Chest X-raycited by 19
  12. 2021Anatomy-XNet: An Anatomy Aware Convolutional Neural Network for Thoracic Disease Classification in Chest X-rayscited by 11
  13. 2022Radiomics-Guided Global-Local Transformer for Weakly Supervised Pathology Localization in Chest X-Rayscited by 7
  14. 2022Consistency-Based Semi-supervised Evidential Active Learning for Diagnostic Radiograph Classificationcited by 7
  15. 2018A Generic Approach to Lung Field Segmentation from Chest Radiographs using Deep Space and Shape Learningcited by 5

Built on by

Not cited yet.