Breast Cancer Histology Image Classification

8 papers with code • 1 benchmarks • 2 datasets

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Most implemented papers

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.

Two-Stage Convolutional Neural Network for Breast Cancer Histology Image Classification

ImagingLab/ICIAR2018 11 Mar 2018

This paper explores the problem of breast tissue classification of microscopy images.

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.

Magnification Generalization for Histopathology Image Embedding

bghojogh/Histopathology-Magnification-Generalization 18 Jan 2021

However, a useful task in histopathology embedding is to train an embedding space regardless of the magnification level.

MCUa: Multi-level Context and Uncertainty aware Dynamic Deep Ensemble for Breast Cancer Histology Image Classification

zakariasenousy/mcua-model 24 Aug 2021

It exploits the high sensitivity to the multi-level contextual information using an uncertainty quantification component to accomplish a novel dynamic ensemble model. MCUamodelhas achieved a high accuracy of 98. 11% on a breast cancer histology image dataset.

Magnification Prior: A Self-Supervised Method for Learning Representations on Breast Cancer Histopathological Images

prakashchhipa/magnification-prior-self-supervised-method 15 Mar 2022

This work presents a novel self-supervised pre-training method to learn efficient representations without labels on histopathology medical images utilizing magnification factors.

VGGIN-Net: Deep Transfer Network for Imbalanced Breast Cancer Dataset


In this paper, we have presented a novel deep neural network architecture involving transfer learning approach, formed by freezing and concatenating all the layers till block4 pool layer of VGG16 pre-trained model (at the lower level) with the layers of a randomly initialized naïve Inception block module (at the higher level).

BCI: Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pix

bupt-ai-cz/BCI 25 Apr 2022

The evaluation of human epidermal growth factor receptor 2 (HER2) expression is essential to formulate a precise treatment for breast cancer.