# Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge

arXiv: https://arxiv.org/abs/2501.15588 · HTML: https://mlsanity.com/paper/2501.15588/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2025.

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

- [US-JEPA: A Joint Embedding Predictive Architecture for Ultrasound](https://mlsanity.com/paper/2602.19322/) (2026)
- [Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development](https://mlsanity.com/paper/2603.27460/) (2026)
- [MTCNet: Motion and Topology Consistency Guided Learning for Mitral Valve Segmentationin 4D Ultrasound](https://mlsanity.com/paper/2507.00660/) (2025)
- [GaussianPile: A Unified Sparse Gaussian Splatting Framework for Slice-based Volumetric Reconstruction](https://mlsanity.com/paper/2603.20611/) (2026)
- [Steer the Sampling, Not the Kernel Grid: Geometry-Guided Sampling Operator for Volumetric Segmentation](https://mlsanity.com/paper/2608.25819/) (2026)

## Abstract (arXiv)

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://tdsc-abus2023.grand-challenge.org/ to benchmark and inspire future developments in algorithmic research.