# Region Proposals for Saliency Map Refinement for Weakly-supervised Disease Localisation and Classification

arXiv: https://arxiv.org/abs/2005.10550 · HTML: https://mlsanity.com/paper/2005.10550/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2020.

## Built on by (most cited first, 15 of 15)

- [Deep Learning for Chest X-ray Analysis: A Survey](https://mlsanity.com/paper/2103.08700/) (2021)
- [XProtoNet: Diagnosis in Chest Radiography with Global and Local Explanations](https://mlsanity.com/paper/2103.10663/) (2021)
- [Delving into Masked Autoencoders for Multi-Label Thorax Disease Classification](https://mlsanity.com/paper/2210.12843/) (2022)
- [Label-Assemble: Leveraging Multiple Datasets with Partial Labels](https://mlsanity.com/paper/2109.12265/) (2021)
- [Self-supervised Mean Teacher for Semi-supervised Chest X-ray Classification](https://mlsanity.com/paper/2103.03629/) (2021)
- [Knowledge Distillation to Ensemble Global and Interpretable Prototype-Based Mammogram Classification Models](https://mlsanity.com/paper/2209.12420/) (2022)
- [NVUM: Non-Volatile Unbiased Memory for Robust Medical Image Classification](https://mlsanity.com/paper/2103.04053/) (2021)
- [BoMD: Bag of Multi-label Descriptors for Noisy Chest X-ray Classification](https://mlsanity.com/paper/2203.01937/) (2022)
- [HydraViT: Adaptive Multi-Branch Transformer for Multi-Label Disease Classification from Chest X-ray Images](https://mlsanity.com/paper/2310.06143/) (2023)
- [Learning Low-Rank Feature for Thorax Disease Classification](https://mlsanity.com/paper/2404.18933/) (2024)
- [Cross- and Intra-image Prototypical Learning for Multi-label Disease Diagnosis and Interpretation](https://mlsanity.com/paper/2411.04607/) (2024)
- [GREN: Graph-Regularized Embedding Network for Weakly-Supervised Disease Localization in X-ray Images](https://mlsanity.com/paper/2107.06442/) (2021)
- [Long-tailed multi-label classification with noisy label of thoracic diseases from chest X-ray](https://mlsanity.com/paper/2311.17334/) (2023)
- [Explainable Transformer Prototypes for Medical Diagnoses](https://mlsanity.com/paper/2403.06961/) (2024)
- [Weighing Features of Lung and Heart Regions for Thoracic Disease Classification](https://mlsanity.com/paper/2105.12430/) (2021)

## Abstract (arXiv)

The deployment of automated systems to diagnose diseases from medical images is challenged by the requirement to localise the diagnosed diseases to justify or explain the classification decision. This requirement is hard to fulfil because most of the training sets available to develop these systems only contain global annotations, making the localisation of diseases a weakly supervised approach. The main methods designed for weakly supervised disease classification and localisation rely on saliency or attention maps that are not specifically trained for localisation, or on region proposals that can not be refined to produce accurate detections. In this paper, we introduce a new model that combines region proposal and saliency detection to overcome both limitations for weakly supervised disease classification and localisation. Using the ChestX-ray14 data set, we show that our proposed model establishes the new state-of-the-art for weakly-supervised disease diagnosis and localisation.