knowledge-base-1

Breast Cancer Detection

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in breast-cancer-detection-6

Trend

Dataset

Best Model

Actions

Breast Cancer Coimbra Data Set

Breast cancer Wisconsin_class 4

Libraries

i

Use these libraries to find breast-cancer-detection-6 models and implementations

richardobi/medigan

2 papers 119

Datasets

BCI

Subtasks

No subtasks available.

Most implemented papers

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

F. M. Calisto, N. Nunes, J. Nascimento•Mon Apr 06 2020

The field research, design and comparative deployment of a multimodal medical imaging user interface for breast screening is described and recommendations from the radiologists for guiding the future design of medical imaging interfaces are summarized.

44

Content

CMMD

BreakHis

CMMD

Breast Lesion Detection in Ultrasound Videos (CVA-Net)

BreastClassifications4

0

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Deep Learning to Improve Breast Cancer Early Detection on Screening Mammography

Li Shen•Mon Dec 31 2018

A deep learning algorithm that can accurately detect breast cancer on screening mammograms using an "end-to-end" training approach that leverages training datasets with either complete clinical annotation or only the cancer status of the whole image, eliminating the reliance on rarely available lesion annotations.

9 0

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Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis

A. Rakhlin, Alexandr A Kalinin, V. Iglovikov, Alexey A. Shvets•Thu Feb 01 2018

This work develops the computational approach based on deep convolution neural networks for breast cancer histology image classification that outperforms other common methods in automated histopathological image classification.

336 0

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

Krzysztof J. Geras, Kyunghyun Cho, Stacey Wolfson, S. G. Kim, L. Moy•Mon Mar 20 2017

This work proposes to use a multi-view deep convolutional neural network that handles a set of high-resolution medical images, and evaluates it on large-scale mammography-based breast cancer screening (BI-RADS prediction) using 886,000 images.

239 0

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CEIMVEN: An Approach of Cutting Edge Implementation of Modified Versions of EfficientNet (V1-V2) Architecture for Breast Cancer Detection and Classification from Ultrasound Images

Sheekar Banerjee, Md. Kamrul Hasan Monir•Thu Aug 24 2023

This research focused mostly on the rigorous implementations and iterative result analysis of different cutting-edge modified versions of EfficientNet architectures namely EfficientNet-V1 and EfficientNet-V2 with ultrasound image, named as CEIMVEN, and utilized transfer learning approach here for using the pre-trained models of EfficientNet versions.

5 0

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Detecting and classifying lesions in mammograms with Deep Learning

D. Ribli, A. Horváth, Z. Unger, P. Pollner, I. Csabai•Tue Jul 25 2017

A CAD system based on one of the most successful object detection frameworks, Faster R-CNN, that detects and classifies malignant or benign lesions on a mammogram without any human intervention is proposed.

629 0

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On breast cancer detection: an application of machine learning algorithms on the wisconsin diagnostic dataset

Abien Fred Agarap•Sun Nov 19 2017

A comparison of six machine learning algorithms: GRU-SVM, Linear Regression, Multilayer Perceptron (MLP), Nearest Neighbor (NN) search, Softmaxregression, and Support Vector Machine on the Wisconsin Diagnostic Breast Cancer dataset by measuring their classification test accuracy, and their sensitivity and specificity values.

249 0

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Conditional Infilling GANs for Data Augmentation in Mammogram Classification

William Lotter, David D. Cox, E. Wu, Kevin Wu•Fri Jul 20 2018

This work trains a class-conditional GAN to perform contextual in-filling, which is then used to synthesize lesions onto healthy screening mammograms and shows that GANs are capable of generating high-resolution synthetic mammogram patches.

145 0

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Regression Concept Vectors for Bidirectional Explanations in Histopathology

V. Andrearczyk, Mara Graziani, H. Müller•Wed Sep 19 2018

This work proposes a methodology to exploit continuous concept measures as Regression Concept Vectors (RCVs) in the activation space of a layer to exploit network sensitivity to increasing values of a given concept measure.

85 0

Paper Graph

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