# BI-RADS-Net: An Explainable Multitask Learning Approach for Cancer Diagnosis in Breast Ultrasound Images

arXiv: https://arxiv.org/abs/2110.04069 · HTML: https://mlsanity.com/paper/2110.04069/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 26 references that are in the map.
- First posted on arXiv 5 Oct 2021.

## Builds on (most cited first, 12 of 12)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Interpretable machine learning: definitions, methods, and applications](https://mlsanity.com/paper/1901.04592/) (2019)
- [On the Automatic Generation of Medical Imaging Reports](https://mlsanity.com/paper/1711.08195/) (2017)
- [What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use](https://mlsanity.com/paper/1905.05134/) (2019)
- [MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network](https://mlsanity.com/paper/1707.02485/) (2017)
- [An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization](https://mlsanity.com/paper/2002.07613/) (2020)
- [Generation of Multimodal Justification Using Visual Word Constraint Model for Explainable Computer-Aided Diagnosis](https://mlsanity.com/paper/1906.03922/) (2019)
- [Multiple Instance Learning for Heterogeneous Images: Training a CNN for Histopathology](https://mlsanity.com/paper/1806.05083/) (2018)
- [Autofocus Layer for Semantic Segmentation](https://mlsanity.com/paper/1805.08403/) (2018)
- [Respond-CAM: Analyzing Deep Models for 3D Imaging Data by Visualizations](https://mlsanity.com/paper/1806.00102/) (2018)
- [DeepMiner: Discovering Interpretable Representations for Mammogram Classification and Explanation](https://mlsanity.com/paper/1805.12323/) (2018)
- [ICADx: Interpretable computer aided diagnosis of breast masses](https://mlsanity.com/paper/1805.08960/) (2018)

## Built on by (most cited first, 7 of 7)

- [HoVer-Trans: Anatomy-aware HoVer-Transformer for ROI-free Breast Cancer Diagnosis in Ultrasound Images](https://mlsanity.com/paper/2205.08390/) (2022)
- [Learning a Clinically-Relevant Concept Bottleneck for Lesion Detection in Breast Ultrasound](https://mlsanity.com/paper/2407.00267/) (2024)
- [Post-Hoc Explainability of BI-RADS Descriptors in a Multi-task Framework for Breast Cancer Detection and Segmentation](https://mlsanity.com/paper/2308.14213/) (2023)
- [Spatially Grounded Concept Bottleneck Models for Trustworthy Breast Ultrasound Diagnosis](https://mlsanity.com/paper/2607.20691/) (2026)
- [Externally Validated Breast Ultrasound Segmentation via Multi-task Learning with BI-RADS-Consistent Morphological Priors](https://mlsanity.com/paper/2511.15968/) (2025)
- [UltrasoundAgents: Hierarchical Multi-Agent Evidence-Chain Reasoning for Breast Ultrasound Diagnosis](https://mlsanity.com/paper/2603.10852/) (2026)
- [TRACE: Training-time Report-guided and Clinically Ordered Concept Editing](https://mlsanity.com/paper/2608.20809/) (2026)

## Abstract (arXiv)

In healthcare, it is essential to explain the decision-making process of machine learning models to establish the trustworthiness of clinicians. This paper introduces BI-RADS-Net, a novel explainable deep learning approach for cancer detection in breast ultrasound images. The proposed approach incorporates tasks for explaining and classifying breast tumors, by learning feature representations relevant to clinical diagnosis. Explanations of the predictions (benign or malignant) are provided in terms of morphological features that are used by clinicians for diagnosis and reporting in medical practice. The employed features include the BI-RADS descriptors of shape, orientation, margin, echo pattern, and posterior features. Additionally, our approach predicts the likelihood of malignancy of the findings, which relates to the BI-RADS assessment category reported by clinicians. Experimental validation on a dataset consisting of 1,192 images indicates improved model accuracy, supported by explanations in clinical terms using the BI-RADS lexicon.