A large-scale multicenter breast cancer DCE-MRI benchmark dataset with expert segmentations

Radiomics: extracting more information from medical images using advanced feature analysis

Lambin, P. et al · 2012

Earlier work this paper cites.

Locally advanced breast cancer: MR imaging for prediction of response to neoadjuvant chemotherapy—results from ACRIN 6657/I-SPY trial

Hylton, N. M. et al · 2012

Earlier work this paper cites.

The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository

Clark, K. et al · 2013

Earlier work this paper cites.

Using computer-extracted image phenotypes from tumors on breast MRI to predict stage [Data set]

Morris, E. et al · 2014

Earlier work this paper cites.

Neoadjuvant chemotherapy for breast cancer: functional tumor volume by MR imaging predicts recurrence-free survival—results from the ACRIN 6657/CALGB 150007 I-SPY 1 trial

Hylton, N. M. et al · 2016

Earlier work this paper cites.

multicenter breast DCE-MRI data and segmentations from patients in the I-SPY 1/ACRIN 6657 trials

Newitt, D., Hylton, N. et al · 2016

Earlier work this paper cites.

Single site breast DCE-MRI data and segmentations from patients undergoing neoadjuvant chemotherapy (version 3) [Data set]

Newitt, D. & Hylton, N · 2016

Earlier work this paper cites.

Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA Data set

Li, H. et al · 2016

Earlier work this paper cites.

Quickfacts

Bureau, U. C · 2016

Earlier work this paper cites.

Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features

Bakas, S. et al · 2017

Earlier work this paper cites.

SimpleITK image-analysis notebooks: a collaborative environment for education and reproducible research

Yaniv, Z. et al · 2018

Earlier work this paper cites.

A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features

Saha, A. et al · 2018

Earlier work this paper cites.

Breast MRI: state of the art

Mann, R. M., Cho, N. & Moy, L · 2019

Earlier work this paper cites.

Automated quality control in image segmentation: application to the uk biobank cardiovascular magnetic resonance imaging study

Robinson, R. et al · 2019

Cited alongside, same era.

Multi-centre, multi-vendor and multi-disease cardiac segmentation: the M&Ms challenge

Campello, V. M. et al · 2021

Cited alongside, same era.

nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation

Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J. & Maier-Hein, K. H · 2021

Cited alongside, same era.

ACRIN 6698/I-SPY2 breast DWI [Data set]

Newitt, D. C. et al · 2021

Cited alongside, same era.

Dynamic contrast-enhanced magnetic resonance images of breast cancer patients with tumor locations [Data set]

Saha, A. et al · 2021

Cited alongside, same era.

Robustness of radiomics to variations in segmentation methods in multimodal brain MRI

Using machine learning to reduce the need for contrast agents in breast MRI through synthetic images

Müller-Franzes, G. et al · 2023

Later among the works it cites.

Segment anything

Kirillov, A. et al · 2023

Later among the works it cites.

BreastSAM: A study of segment anything model for breast tumor detection in ultrasound images

Hu, M., Li, Y. & Yang, X · 2023

Later among the works it cites.

Mammo-SAM: Adapting foundation segment anything model for automatic breast mass segmentation in whole mammograms

Xiong, X., Wang, C., Li, W. & Li, G · 2023

Later among the works it cites.

Pre- to post-contrast breast MRI synthesis for enhanced tumour segmentation

Osuala, R. et al · 2024

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Towards learning contrast kinetics with multi-condition latent diffusion models

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