# Learning a Clinically-Relevant Concept Bottleneck for Lesion Detection in Breast Ultrasound

arXiv: https://arxiv.org/abs/2407.00267 · HTML: https://mlsanity.com/paper/2407.00267/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 27 references that are in the map.
- First posted on arXiv 29 Jun 2024.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Concept Bottleneck Models](https://mlsanity.com/paper/2007.04612/) (2020)
- [The VIA Annotation Software for Images, Audio and Video](https://mlsanity.com/paper/1904.10699/) (2019)
- [Do Concept Bottleneck Models Learn as Intended?](https://mlsanity.com/paper/2105.04289/) (2021)
- [A New Dataset and A Baseline Model for Breast Lesion Detection in Ultrasound Videos](https://mlsanity.com/paper/2207.00141/) (2022)
- [Joint Weakly and Semi-Supervised Deep Learning for Localization and Classification of Masses in Breast Ultrasound Images](https://mlsanity.com/paper/1710.03778/) (2017)
- [BI-RADS-Net: An Explainable Multitask Learning Approach for Cancer Diagnosis in Breast Ultrasound Images](https://mlsanity.com/paper/2110.04069/) (2021)
- [A Spatial-Temporal Deformable Attention based Framework for Breast Lesion Detection in Videos](https://mlsanity.com/paper/2309.04702/) (2023)
- [Gradually Applying Weakly Supervised and Active Learning for Mass Detection in Breast Ultrasound Images](https://mlsanity.com/paper/2008.08416/) (2020)

## Built on by (most cited first, 4 of 4)

- [Spatially Grounded Concept Bottleneck Models for Trustworthy Breast Ultrasound Diagnosis](https://mlsanity.com/paper/2607.20691/) (2026)
- [Vision-Language Models Encode Clinical Guidelines for Concept-Based Medical Reasoning](https://mlsanity.com/paper/2603.08921/) (2026)
- [TRACE: A Concept Bottleneck Model for Longitudinal 3D Glioblastoma Response Assessment](https://mlsanity.com/paper/2606.30313/) (2026)
- [Loss Invariance Determines What Concept Layers Encode: Volume Grounding in Echocardiography](https://mlsanity.com/paper/2607.25748/) (2026)

## Abstract (arXiv)

Detecting and classifying lesions in breast ultrasound images is a promising application of artificial intelligence (AI) for reducing the burden of cancer in regions with limited access to mammography. Such AI systems are more likely to be useful in a clinical setting if their predictions can be explained to a radiologist. This work proposes an explainable AI model that provides interpretable predictions using a standard lexicon from the American College of Radiology's Breast Imaging and Reporting Data System (BI-RADS). The model is a deep neural network featuring a concept bottleneck layer in which known BI-RADS features are predicted before making a final cancer classification. This enables radiologists to easily review the predictions of the AI system and potentially fix errors in real time by modifying the concept predictions. In experiments, a model is developed on 8,854 images from 994 women with expert annotations and histological cancer labels. The model outperforms state-of-the-art lesion detection frameworks with 48.9 average precision on the held-out testing set, and for cancer classification, concept intervention is shown to increase performance from 0.876 to 0.885 area under the receiver operating characteristic curve. Training and evaluation code is available at https://github.com/hawaii-ai/bus-cbm.