Double Transfer Learning for Breast Cancer Histopathologic Image Classification
This incremental improvement addresses classification accuracy for breast cancer diagnosis, specifically for histopathologic images, by removing irrelevant patches.
The paper tackles breast cancer histopathologic image classification by using double transfer learning: first for feature extraction with Inception-v3 and second to filter irrelevant patches via an SVM trained on colorectal cancer data, improving accuracy by 3.7% and an additional 0.7%.
This work proposes a classification approach for breast cancer histopathologic images (HI) that uses transfer learning to extract features from HI using an Inception-v3 CNN pre-trained with ImageNet dataset. We also use transfer learning on training a support vector machine (SVM) classifier on a tissue labeled colorectal cancer dataset aiming to filter the patches from a breast cancer HI and remove the irrelevant ones. We show that removing irrelevant patches before training a second SVM classifier, improves the accuracy for classifying malign and benign tumors on breast cancer images. We are able to improve the classification accuracy in 3.7% using the feature extraction transfer learning and an additional 0.7% using the irrelevant patch elimination. The proposed approach outperforms the state-of-the-art in three out of the four magnification factors of the breast cancer dataset.