Breast Cancer Detection

24 papers with code • 3 benchmarks • 6 datasets

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Most implemented papers

BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis

MIMBCD-UI/prototype-multi-modality 7 Apr 2020

This paper describes the field research, design and comparative deployment of a multimodal medical imaging user interface for breast screening.

Deep Learning to Improve Breast Cancer Early Detection on Screening Mammography

lishen/end2end-all-conv 30 Aug 2017

We also demonstrate that a whole image classifier trained using our end-to-end approach on the DDSM digitized film mammograms can be transferred to INbreast FFDM images using only a subset of the INbreast data for fine-tuning and without further reliance on the availability of lesion annotations.

Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis

alexander-rakhlin/ICIAR2018 2 Feb 2018

In this work, we develop the computational approach based on deep convolution neural networks for breast cancer histology image classification.

High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks

nyukat/BIRADS_classifier 21 Mar 2017

In our work, we propose to use a multi-view deep convolutional neural network that handles a set of high-resolution medical images.

Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening

nyukat/breast_cancer_classifier 20 Mar 2019

We present a deep convolutional neural network for breast cancer screening exam classification, trained and evaluated on over 200, 000 exams (over 1, 000, 000 images).

Detecting and classifying lesions in mammograms with Deep Learning

riblidezso/frcnn_cad 26 Jul 2017

In the last two decades Computer Aided Diagnostics (CAD) systems were developed to help radiologists analyze screening mammograms.

Conditional Infilling GANs for Data Augmentation in Mammogram Classification

ericwu09/mammo-cigan 21 Jul 2018

Deep learning approaches to breast cancer detection in mammograms have recently shown promising results.

Regression Concept Vectors for Bidirectional Explanations in Histopathology

medgift/iMIMIC-RCVs 9 Apr 2019

Explanations for deep neural network predictions in terms of domain-related concepts can be valuable in medical applications, where justifications are important for confidence in the decision-making.

Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor Infiltrating Lymphocytes in Invasive Breast Cancer

SBU-BMI/quip_cancer_segmentation 26 May 2019

Quantitative assessment of Tumor-TIL spatial relationships is increasingly important in both basic science and clinical aspects of breast cancer research.