Share Email Print

Proceedings Paper

Deep multi-label 3D ConvNet for breast cancer diagnosis in DBT with inversion augmentation
Author(s): Itsara Wichakam; Jatuporn Chayakulkheeree M.D.; Peerapon Vateekul
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

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.

Paper Details

Date Published: 9 August 2018
PDF: 13 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108065P (9 August 2018); doi: 10.1117/12.2503541
Show Author Affiliations
Itsara Wichakam, Chulalongkorn Univ. (Thailand)
Jatuporn Chayakulkheeree M.D., Chulalongkorn Univ. (Thailand)
Peerapon Vateekul, Chulalongkorn Univ. (Thailand)

Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

© SPIE. Terms of Use
Back to Top