Deep multi-label 3D ConvNet for breast cancer diagnosis in DBT with inversion augmentation
Itsara Wichakam, Jatuporn Chayakulkheeree, Peerapon Vateekul · 2018
Digital breast tomosynthesis (DBT) is a pseudo-3D breast image, which is a collection of 2D slice images. It is increasingly being used for detection and diagnosis of breast cancer. In comparison to mammography (2D breast image), DBT provides higher sensitivity (true-positive rate). Recently, there have been many state-of-the-art methods for the detection of masses and calcifications in DBT. However, these previous studies can identify just only one type of breast lesions (abnormality class), while there can be multiple breast lesions simultaneously (multi-label classification); each of them requires different treatment procedures. In this paper, we present an end-to-end multi-label classification approach that takes into account the pseudo-3D nature of DBT and able to automatically detect two major types of malignant lesions: soft tissue and calcifications. The proposed network is a real 3D convolutional network (ConvNet) with two additional strategies: global kernel and global average pooling. Also, an inversion augmentation is invented; it does not only alleviate a small size of training data, but also help an occlusion overlapping issue. Such system can be used to support radiologists in DBT analysis by prompting suspicious locations. Our in-house dataset consists of 115 DBT volumes, including 91 volumes of cancer detected cases (contained biopsy-proven malignant lesions) and 24 volumes of normal cases. The experimental results show that our approach yields a promising result for the classification of malignant lesions: 72% accuracy with f1-score at 0.842. Moreover, a multi-label classification network is able to detect concurrent lesions in 3 of 6 volumes in the testing set.