# Low-Resolution Chest X-ray Classification via Knowledge Distillation and Multi-task Learning

arXiv: https://arxiv.org/abs/2405.13370 · HTML: https://mlsanity.com/paper/2405.13370/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 26 references that are in the map.
- First posted on arXiv 22 May 2024.

## Builds on (most cited first, 13 of 13)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [FitNets: Hints for Thin Deep Nets](https://mlsanity.com/paper/1412.6550/) (2014)
- [Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer](https://mlsanity.com/paper/1612.03928/) (2016)
- [Improved Knowledge Distillation via Teacher Assistant](https://mlsanity.com/paper/1902.03393/) (2019)
- [Heterogeneous Knowledge Distillation using Information Flow Modeling](https://mlsanity.com/paper/2005.00727/) (2020)
- [ALP-KD: Attention-Based Layer Projection for Knowledge Distillation](https://mlsanity.com/paper/2012.14022/) (2020)
- [Graph-based Knowledge Distillation by Multi-head Attention Network](https://mlsanity.com/paper/1907.02226/) (2019)
- [Circumventing Outliers of AutoAugment with Knowledge Distillation](https://mlsanity.com/paper/2003.11342/) (2020)
- [AI can evolve without labels: self-evolving vision transformer for chest X-ray diagnosis through knowledge distillation](https://mlsanity.com/paper/2202.06431/) (2022)
- [Explainable Knowledge Distillation for On-device Chest X-Ray Classification](https://mlsanity.com/paper/2305.06244/) (2023)

## Built on by (most cited first, 1 of 1)

- [Weakly Supervised Tuberculosis Localization in Chest X-rays through Knowledge Distillation](https://mlsanity.com/paper/2512.11057/) (2025)

## Abstract (arXiv)

This research addresses the challenges of diagnosing chest X-rays (CXRs) at low resolutions, a common limitation in resource-constrained healthcare settings. High-resolution CXR imaging is crucial for identifying small but critical anomalies, such as nodules or opacities. However, when images are downsized for processing in Computer-Aided Diagnosis (CAD) systems, vital spatial details and receptive fields are lost, hampering diagnosis accuracy. To address this, this paper presents the Multilevel Collaborative Attention Knowledge (MLCAK) method. This approach leverages the self-attention mechanism of Vision Transformers (ViT) to transfer critical diagnostic knowledge from high-resolution images to enhance the diagnostic efficacy of low-resolution CXRs. MLCAK incorporates local pathological findings to boost model explainability, enabling more accurate global predictions in a multi-task framework tailored for low-resolution CXR analysis. Our research, utilizing the Vindr CXR dataset, shows a considerable enhancement in the ability to diagnose diseases from low-resolution images (e.g. 28 x 28), suggesting a critical transition from the traditional reliance on high-resolution imaging (e.g. 224 x 224).