Breast Cancer Histology Image Classification
8 papers with code • 1 benchmarks • 2 datasets
Latest papers with no code
Rotation-Agnostic Image Representation Learning for Digital Pathology
This paper addresses complex challenges in histopathological image analysis through three key contributions.
Attention-Map Augmentation for Hypercomplex Breast Cancer Classification
In this step, a parameterized hypercomplex neural network (PHNN) is employed to perform breast cancer classification.
Magnification Invariant Medical Image Analysis: A Comparison of Convolutional Networks, Vision Transformers, and Token Mixers
Convolution Neural Networks (CNNs) are widely used in medical image analysis, but their performance degrade when the magnification of testing images differ from the training images.
Hierarchical ResNeXt Models for Breast Cancer Histology Image Classification
Microscopic histology image analysis is a cornerstone in early detection of breast cancer.
Deep Learning Framework for Multi-class Breast Cancer Histology Image Classification
In this work, we present a deep learning framework for multi-class breast cancer image classification as our submission to the International Conference on Image Analysis and Recognition (ICIAR) 2018 Grand Challenge on BreAst Cancer Histology images (BACH).