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

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%. To improve the classification performance, a random forest 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% (best accuracy 85.00%). When handcrafted features are combined with the five deep learning feature groups, the average accuracy is improved to 87.10% (best accuracy 93.00%)

Optimization of Convolutional Neural Network ensemble classifiers by Genetic Algorithms

Breast cancer exhibits a high mortality rate and it is the most invasive cancer in women. An analysis from histopathological images could predict this disease. In this way, computational image processing might support this task. In this work a proposal which employes deep learning convolutional neural networks is presented. Then, an ensemble of networks is considered in order to obtain an enhanced recognition performance of the system by the consensus of the networks of the ensemble. Finally, a genetic algorithm is also considered to choose the networks that belong to the ensemble. The proposal has been tested by carrying out several experiments with a set of benchmark images.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Improve the Performance of Transfer Learning Without Fine-Tuning Using Dissimilarity-Based Multi-view Learning for Breast Cancer Histology Images

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Towards Interactive Breast Tumor Classification Using Transfer Learning

The diagnosis of breast cancer relies on the accurate classification of morphological subtypes in histological sections. Recent advances in image analysis using convolutional neural networks have yielded promising automated methods for this classification task. These networks are usually trained from scratch and depend on hours-long training with thousands of labeled examples to produce good results. Once trained these methods can not easily be adapted in cases of misclassification or to novel tasks. We aim to develop methods that can quickly be adapted in an interactive way. As a first step in this direction we present a classification method that enables fast training with a limited number of samples and achieves state-of-the-art results