# Using Radiomics as Prior Knowledge for Thorax Disease Classification and Localization in Chest X-rays

arXiv: https://arxiv.org/abs/2011.12506 · HTML: https://mlsanity.com/paper/2011.12506/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2020.

## Built on by (most cited first, 5 of 5)

- [RadBERT-CL: Factually-Aware Contrastive Learning For Radiology Report Classification](https://mlsanity.com/paper/2110.15426/) (2021)
- [Radiomics-Guided Global-Local Transformer for Weakly Supervised Pathology Localization in Chest X-Rays](https://mlsanity.com/paper/2207.04394/) (2022)
- [RoS-KD: A Robust Stochastic Knowledge Distillation Approach for Noisy Medical Imaging](https://mlsanity.com/paper/2210.08388/) (2022)
- [CrossEAI: Using Explainable AI to generate better bounding boxes for Chest X-ray images](https://mlsanity.com/paper/2310.19835/) (2023)
- [MAProtoNet: A Multi-scale Attentive Interpretable Prototypical Part Network for 3D Magnetic Resonance Imaging Brain Tumor Classification](https://mlsanity.com/paper/2404.08917/) (2024)

## Abstract (arXiv)

Chest X-ray becomes one of the most common medical diagnoses due to its noninvasiveness. The number of chest X-ray images has skyrocketed, but reading chest X-rays still have been manually performed by radiologists, which creates huge burnouts and delays. Traditionally, radiomics, as a subfield of radiology that can extract a large number of quantitative features from medical images, demonstrates its potential to facilitate medical imaging diagnosis before the deep learning era. In this paper, we develop an end-to-end framework, ChexRadiNet, that can utilize the radiomics features to improve the abnormality classification performance. Specifically, ChexRadiNet first applies a light-weight but efficient triplet-attention mechanism to classify the chest X-rays and highlight the abnormal regions. Then it uses the generated class activation map to extract radiomic features, which further guides our model to learn more robust image features. After a number of iterations and with the help of radiomic features, our framework can converge to more accurate image regions. We evaluate the ChexRadiNet framework using three public datasets: NIH ChestX-ray, CheXpert, and MIMIC-CXR. We find that ChexRadiNet outperforms the state-of-the-art on both disease detection (0.843 in AUC) and localization (0.679 in T(IoU) = 0.1). We will make the code publicly available at https://github.com/bionlplab/lung_disease_detection_amia2021, with the hope that this method can facilitate the development of automatic systems with a higher-level understanding of the radiological world.