# A large-scale multicenter breast cancer DCE-MRI benchmark dataset with expert segmentations arXiv: https://arxiv.org/abs/2406.13844 ยท HTML: https://mlsanity.com/paper/2406.13844/ ## Key facts - Cited by 40 papers in the map (data through 14 Oct 2026). - Builds on 6 of its 39 references that are in the map. - First posted on arXiv 19 Jun 2024. ## Builds on (most cited first, 6 of 6) - [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023) - [Segment Anything in Medical Images](https://mlsanity.com/paper/2304.12306/) (2023) - [Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging](https://mlsanity.com/paper/2107.09543/) (2021) - [Towards Learning Contrast Kinetics with Multi-Condition Latent Diffusion Models](https://mlsanity.com/paper/2403.13890/) (2024) - [Pre- to Post-Contrast Breast MRI Synthesis for Enhanced Tumour Segmentation](https://mlsanity.com/paper/2311.10879/) (2023) - [BreastSAM: A Study of Segment Anything Model for Breast Tumor Detection in Ultrasound Images](https://mlsanity.com/paper/2305.12447/) (2023) ## Built on by (most cited first, 30 of 40) - [Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning](https://mlsanity.com/paper/2506.07044/) (2025) - [nnInteractive: Redefining 3D Promptable Segmentation](https://mlsanity.com/paper/2503.08373/) (2025) - [Primus: Enforcing Attention Usage for 3D Medical Image Segmentation](https://mlsanity.com/paper/2503.01835/) (2025) - [Fr'echet Radiomic Distance (FRD): A Versatile Metric for Comparing Medical Imaging Datasets](https://mlsanity.com/paper/2412.01496/) (2024) - [A European Multi-Center Breast Cancer MRI Dataset](https://mlsanity.com/paper/2506.00474/) (2025) - [Investigating Label Bias and Representational Sources of Age-Related Disparities in Medical Segmentation](https://mlsanity.com/paper/2511.00477/) (2025) - [HCMA-UNet: A Hybrid CNN-Mamba UNet with Axial Self-Attention for Efficient Breast Cancer Segmentation](https://mlsanity.com/paper/2501.00751/) (2025) - [Federated nnU-Net for Privacy-Preserving Medical Image Segmentation](https://mlsanity.com/paper/2503.02549/) (2025) - [mAIstro: an open-source multi-agentic system for automated end-to-end development of radiomics and deep learning models for medical imaging](https://mlsanity.com/paper/2505.03785/) (2025) - [Single Image Test-Time Adaptation via Multi-View Co-Training](https://mlsanity.com/paper/2506.23705/) (2025) - [Large-scale Multi-sequence Pretraining for Generalizable MRI Analysis in Versatile Clinical Applications](https://mlsanity.com/paper/2508.07165/) (2025) - [Comparing Conditional Diffusion Models for Synthesizing Contrast-Enhanced Breast MRI from Pre-Contrast Images](https://mlsanity.com/paper/2508.13776/) (2025) - [LesiOnTime -- Joint Temporal and Clinical Modeling for Small Breast Lesion Segmentation in Longitudinal DCE-MRI](https://mlsanity.com/paper/2508.00496/) (2025) - [MeisenMeister: A Simple Two Stage Pipeline for Breast Cancer Classification on MRI](https://mlsanity.com/paper/2510.27326/) (2025) - [The MAMA-MIA Challenge: Advancing Generalizability and Fairness in Breast MRI Tumor Segmentation and Treatment Response Prediction](https://mlsanity.com/paper/2603.01250/) (2026) - [Large Kernel MedNeXt for Breast Tumor Segmentation and Self-Normalizing Network for pCR Classification in Magnetic Resonance Images](https://mlsanity.com/paper/2508.01831/) (2025) - [R2Seg: Training-Free OOD Medical Tumor Segmentation via Anatomical Reasoning and Statistical Rejection](https://mlsanity.com/paper/2511.12691/) (2025) - [Segmentation variability and radiomics stability for predicting Triple-Negative Breast Cancer subtype using Magnetic Resonance Imaging](https://mlsanity.com/paper/2504.01692/) (2025) - [Improving Prostate Gland Segmentation Using Transformer based Architectures](https://mlsanity.com/paper/2506.14844/) (2025) - [Towards Affordable Tumor Segmentation and Visualization for 3D Breast MRI Using SAM2](https://mlsanity.com/paper/2507.23272/) (2025) - [Acquisition Time-Informed Breast Tumor Segmentation from Dynamic Contrast-Enhanced MRI](https://mlsanity.com/paper/2511.16498/) (2025) - [Agent-Based Output Drift Detection for Breast Cancer Response Prediction in a Multisite Clinical Decision Support System](https://mlsanity.com/paper/2512.18450/) (2025) - [Selective Phase-Aware Training of nnU-Net for Robust Breast Cancer Segmentation in Multi-Center DCE-MRI](https://mlsanity.com/paper/2512.19225/) (2025) - [Field strength-dependent performance variability in deep learning-based analysis of magnetic resonance imaging](https://mlsanity.com/paper/2512.22176/) (2025) - [Scaling Ultrasound Volumetric Reconstruction via Mobile Augmented Reality](https://mlsanity.com/paper/2602.18500/) (2026) - [Multimodal Stepwise Clinically-Guided Attention Learning for Pathological Complete Response Prediction in Breast Cancer](https://mlsanity.com/paper/2605.07561/) (2026) - [Robustness of breast lesion segmentation under MRI undersampling improves with k-space-aware deep learning](https://mlsanity.com/paper/2605.22327/) (2026) - [Federated Medical Image Segmentation under Real-World Label Noise: A Benchmark Suite for Noisy Label Learning Method Selection](https://mlsanity.com/paper/2606.16868/) (2026) - [Cross-Dataset Generalization in Breast MRI Tumor Classification via Class-Wise Dataset Mixing](https://mlsanity.com/paper/2607.18678/) (2026) - [MIRAGE: Multi-scale Lesion-Informed Representation with Auxiliary Guidance for MRI Contrast Enhancement](https://mlsanity.com/paper/2607.19137/) (2026) ## Abstract (arXiv) Artificial Intelligence (AI) research in breast cancer Magnetic Resonance Imaging (MRI) faces challenges due to limited expert-labeled segmentations. To address this, we present a multicenter dataset of 1506 pre-treatment T1-weighted dynamic contrast-enhanced MRI cases, including expert annotations of primary tumors and non-mass-enhanced regions. The dataset integrates imaging data from four collections in The Cancer Imaging Archive (TCIA), where only 163 cases with expert segmentations were initially available. To facilitate the annotation process, a deep learning model was trained to produce preliminary segmentations for the remaining cases. These were subsequently corrected and verified by 16 breast cancer experts (averaging 9 years of experience), creating a fully annotated dataset. Additionally, the dataset includes 49 harmonized clinical and demographic variables, as well as pre-trained weights for a baseline nnU-Net model trained on the annotated data. This resource addresses a critical gap in publicly available breast cancer datasets, enabling the development, validation, and benchmarking of advanced deep learning models, thus driving progress in breast cancer diagnostics, treatment response prediction, and personalized care.