mlsanityData to 14 Oct 2026

Weakly Supervised Tuberculosis Localization in Chest X-rays through Knowledge Distillation

arXiv 2512.11057 ↗First posted 11 Dec 2025cs.CVcited by 0

On the map: Knowledge distillation → Distillation for medical imaging

Abstract (arXiv)

Tuberculosis (TB) remains one of the leading causes of mortality worldwide, particularly in resource-limited countries. Chest X-ray (CXR) imaging serves as an accessible and cost-effective diagnostic tool but requires expert interpretation, which is often unavailable. Although machine learning models have shown high performance in TB classification, they often depend on spurious correlations and fail to generalize. Besides, building large datasets featuring high-quality annotations for medical images demands substantial resources and input from domain specialists, and typically involves several annotators reaching agreement, which results in enormous financial and logistical expenses. This study repurposes knowledge distillation technique to train CNN models reducing spurious correlations and localize TB-related abnormalities without requiring bounding-box annotations. By leveraging a teacher-student framework with ResNet50 architecture, the proposed method trained on TBX11k dataset achieve impressive 0.2428 mIOU score. Experimental results further reveal that the student model consistently outperforms the teacher, underscoring improved robustness and potential for broader clinical deployment in diverse settings.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Distillation for medical imaging, one of 12 directions in Knowledge distillation. Builds on 21 of its 36 references that are in the map. First posted on arXiv 11 Dec 2025.

Builds on

21 of 36 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023Explainable Knowledge Distillation for On-device Chest X-Ray Classification10 May
  2. 2022AI can evolve without labels: self-evolving vision transformer for chest X-ray diagnosis through knowledge distillation13 Feb

Other references · 19

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  4. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  5. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,733
  6. 2019RandAugment: Practical automated data augmentation with a reduced search spacecited by 2,269
  7. 2020Shortcut Learning in Deep Neural Networkscited by 1,969
  8. 2019CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparisoncited by 1,423
  9. 2016On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minimacited by 1,352
  10. 2024VMamba: Visual State Space Modelcited by 969
  11. 2020Captum: A unified and generic model interpretability library for PyTorchcited by 547
  12. 2019PyHessian: Neural Networks Through the Lens of the Hessiancited by 292
7 more
  1. 2020Annotation-efficient deep learning for automatic medical image segmentationcited by 79
  2. 2022What Makes Transfer Learning Work For Medical Images: Feature Reuse & Other Factorscited by 61
  3. 2020Deep Learning Applied to Chest X-Rays: Exploiting and Preventing Shortcutscited by 29
  4. 2016Local minima in training of neural networkscited by 19
  5. 2023Revisiting Computer-Aided Tuberculosis Diagnosiscited by 6
  6. 2021Rethinking Annotation Granularity for Overcoming Shortcuts in Deep Learning-based Radiograph Diagnosis: A Multicenter Studycited by 3
  7. 2024Low-Resolution Chest X-ray Classification via Knowledge Distillation and Multi-task Learningcited by 1

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025Explainable Knowledge Distillation for Efficient Medical Image Classification
  2. 2025Benchmarking Chest X-ray Diagnosis Models Across Multinational Datasets
  3. 2023You've Got Two Teachers: Co-evolutionary Image and Report Distillation for Semi-supervised Anatomical Abnormality Detection in Chest X-ray
  4. 2025CLARiTy: A Vision Transformer for Multi-Label Classification and Weakly-Supervised Localization of Chest X-ray Pathologies
  5. 2022Probabilistic Integration of Object Level Annotations in Chest X-ray Classification
  6. 2025DCSNet: A Lightweight Knowledge Distillation-Based Model with Explainable AI for Lung Cancer Diagnosis from Histopathological Images
  7. 2024Bootstrapping Chest CT Image Understanding by Distilling Knowledge from X-ray Expert Models
  8. 2025VICCA: Visual Interpretation and Comprehension of Chest X-ray Anomalies in Generated Report Without Human Feedback