Detection of masses and architectural distortions in digital breast tomosynthesis: a publicly available dataset of 5,060 patients and a deep learning model

13 November 2020

Papers citing "Detection of masses and architectural distortions in digital breast tomosynthesis: a publicly available dataset of 5,060 patients and a deep learning model"

8 / 8 papers shown

Title
SIFT-DBT: Self-supervised Initialization and Fine-Tuning for Imbalanced Digital Breast Tomosynthesis Image Classification Yuexi Du R. Hooley John Lewin Nicha Dvornek 17 1 0 19 Mar 2024
Convolutional Neural Networks Rarely Learn Shape for Semantic Segmentation Yixin Zhang Maciej Mazurowski 3DV 3DPC 30 8 0 11 May 2023
Unsupervised anomaly localization in high-resolution breast scans using deep pluralistic image completion N. Konz Haoyu Dong Maciej Mazurowski 24 2 0 04 May 2023
Multi-Head Feature Pyramid Networks for Breast Mass Detection Hexiang Zhang Zhenghua Xu Dan Yao Shuo Zhang Junyang Chen Thomas Lukasiewicz 21 3 0 22 Feb 2023
A survey on deep learning approaches for breast cancer diagnosis Timothy C. H. Kwong S. Mazaheri MedIm 27 4 0 18 Sep 2021
Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging Richard Osuala Kaisar Kushibar Lidia Garrucho Akis Linardos Zuzanna Szafranowska Stefan Klein Ben Glocker Oliver Díaz Karim Lekadir MedIm 39 42 0 20 Jul 2021
A Systematic Collection of Medical Image Datasets for Deep Learning Johann Li Guangming Zhu Cong Hua Mingtao Feng Basheer Bennamoun ... Xu Xu Lin Mei Liang Zhang Syed Afaq Ali Shah Bennamoun OOD 41 49 0 24 Jun 2021
A Survey on Deep Learning in Medical Image Analysis G. Litjens Thijs Kooi B. Bejnordi A. Setio F. Ciompi Mohsen Ghafoorian Jeroen van der Laak Bram van Ginneken C. I. Sánchez OOD 310 10,621 0 19 Feb 2017