# Post-Hoc Explainability of BI-RADS Descriptors in a Multi-task Framework for Breast Cancer Detection and Segmentation

arXiv: https://arxiv.org/abs/2308.14213 · HTML: https://mlsanity.com/paper/2308.14213/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 24 references that are in the map.
- First posted on arXiv 27 Aug 2023.

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

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization](https://mlsanity.com/paper/2002.07613/) (2020)
- [Vision Transformer for Classification of Breast Ultrasound Images](https://mlsanity.com/paper/2110.14731/) (2021)
- [Generation of Multimodal Justification Using Visual Word Constraint Model for Explainable Computer-Aided Diagnosis](https://mlsanity.com/paper/1906.03922/) (2019)
- [DeepMiner: Discovering Interpretable Representations for Mammogram Classification and Explanation](https://mlsanity.com/paper/1805.12323/) (2018)
- [BI-RADS-Net: An Explainable Multitask Learning Approach for Cancer Diagnosis in Breast Ultrasound Images](https://mlsanity.com/paper/2110.04069/) (2021)
- [ICADx: Interpretable computer aided diagnosis of breast masses](https://mlsanity.com/paper/1805.08960/) (2018)
- [Explainable Ensemble Machine Learning for Breast Cancer Diagnosis based on Ultrasound Image Texture Features](https://mlsanity.com/paper/2201.07227/) (2022)
- [Classification of Breast Cancer Lesions in Ultrasound Images by using Attention Layer and loss Ensembles in Deep Convolutional Neural Networks](https://mlsanity.com/paper/2102.11519/) (2021)

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

- [Explainable AI (XAI) in Image Segmentation in Medicine, Industry, and Beyond: A Survey](https://mlsanity.com/paper/2405.01636/) (2024)

## Abstract (arXiv)

Despite recent medical advancements, breast cancer remains one of the most prevalent and deadly diseases among women. Although machine learning-based Computer-Aided Diagnosis (CAD) systems have shown potential to assist radiologists in analyzing medical images, the opaque nature of the best-performing CAD systems has raised concerns about their trustworthiness and interpretability. This paper proposes MT-BI-RADS, a novel explainable deep learning approach for tumor detection in Breast Ultrasound (BUS) images. The approach offers three levels of explanations to enable radiologists to comprehend the decision-making process in predicting tumor malignancy. Firstly, the proposed model outputs the BI-RADS categories used for BUS image analysis by radiologists. Secondly, the model employs multi-task learning to concurrently segment regions in images that correspond to tumors. Thirdly, the proposed approach outputs quantified contributions of each BI-RADS descriptor toward predicting the benign or malignant class using post-hoc explanations with Shapley Values.