Improve the performance of transfer learning without fine-tuning using dissimilarity-based multi-view learning for breast cancer histology images

03/29/2018

∙

Breast cancer is one of the most common types of cancer and leading cancer-related death causes for women. In the context of ICIAR 2018 Grand Challenge on Breast Cancer Histology Images, we compare one handcrafted feature extractor and five transfer learning feature extractors based on deep learning. We find out that the deep learning networks pretrained on ImageNet have better performance than the popular handcrafted features used for breast cancer histology images. The best feature extractor achieves an average accuracy of 79.30 dissimilarity based integration method is used to combine different feature groups together. When the five deep learning feature groups are combined, the average accuracy is improved to 82.90 features are combined with the five deep learning feature groups, the average accuracy is improved to 87.10

READ FULL TEXT

Improve the performance of transfer learning without fine-tuning using dissimilarity-based multi-view learning for breast cancer histology images

A Deep Analysis of Transfer Learning Based Breast Cancer Detection Using Histopathology Images

Self-supervised learning for analysis of temporal and morphological drug effects in cancer cell imaging data

An Efficient Confidence Measure-Based Evaluation Metric for Breast Cancer Screening Using Bayesian Neural Networks

Mammography Dual View Mass Correspondence

Deep Learning Framework for Multi-class Breast Cancer Histology Image Classification

A Semi-Supervised Framework for Automatic Pixel-Wise Breast Cancer Grading of Histological Images

Unsupervised Learning of Deep-Learned Features from Breast Cancer Images

Improve the performance of transfer learning without fine-tuning using dissimilarity-based multi-view learning for breast cancer histology images

Related Research

A Deep Analysis of Transfer Learning Based Breast Cancer Detection Using Histopathology Images

Self-supervised learning for analysis of temporal and morphological drug effects in cancer cell imaging data

An Efficient Confidence Measure-Based Evaluation Metric for Breast Cancer Screening Using Bayesian Neural Networks

Mammography Dual View Mass Correspondence

Deep Learning Framework for Multi-class Breast Cancer Histology Image Classification

A Semi-Supervised Framework for Automatic Pixel-Wise Breast Cancer Grading of Histological Images

Unsupervised Learning of Deep-Learned Features from Breast Cancer Images