Breast Cancer Histology Image Classification
12 papers with code • 2 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in Breast Cancer Histology Image Classification
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BCI
BreakHis
ICIAR 2018 Grand Challenge on Breast Cancer Histology Images
Most implemented papers
Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis
alexander-rakhlin/ICIAR2018
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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
This paper explores the problem of breast tissue classification of microscopy images.
Breast cancer histology classification using Deep Residual Networks
In this work, in order to improve the computer aided diagnosis systems’ performance on histopathological image analysis, we have proposed an approach with image pre-processing followed by a deep learning method to classify the breast cancer histology images into four classes; (i) normal tissue, (ii) benign lesion, (iii) in-situ carcinoma, and (iv) invasive carcinoma.
Regression Concept Vectors for Bidirectional Explanations in Histopathology
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
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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
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
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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
SainiManisha/VGGIN-Net
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IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022
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
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The evaluation of human epidermal growth factor receptor 2 (HER2) expression is essential to formulate a precise treatment for breast cancer.
Classification of Breast Cancer Histopathology Images using a Modified Supervised Contrastive Learning Method
matinamehdizadeh/Breast-Cancer-Detection
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Deep neural networks have reached remarkable achievements in medical image processing tasks, specifically in classifying and detecting various diseases.
