Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring

Abstract

Mammographic risk scoring has commonly been automated by extracting a set of handcrafted features from mammograms, and relating the responses directly or indirectly to breast cancer risk. We present a method that learns a feature hierarchy from unlabeled data. When the learned features are used as the input to a simple classifier, two different tasks can be addressed: i) breast density segmentation, and ii) scoring of mammographic texture. The proposed model learns features at multiple scales. To control the models capacity a novel sparsity regularizer is introduced that incorporates both lifetime and population sparsity. We evaluated our method on three different clinical datasets. Our state-of-the-art results show that the learned breast density scores have a very strong positive relationship with manual ones, and that the learned texture scores are predictive of breast cancer. The model is easy to apply and generalizes to many other segmentation and scoring problems.

Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique

“…Unsupervised feature learning for mammography risk scoring is presented in Kallenberg et al [32]. In this work, a method is shown that learns a feature hierarchy from unlabeled data.…”

Breast Density Classification Using Local Quinary Patterns with Various Neighbourhood Topologies

“…In medical image analysis, deep learning techniques have been used in segmentation and classification problems related to the brain, eye, chest, digital pathology, breast, cardiac, abdomen and musculoskeletal imaging [23]. In the application of breast imaging, particularly in mammography, Kallenberg et al [24] showed the potential of unsupervised deep learning methods applied to breast density segmentation and mammographic risk scoring on three different clinical datasets, and a strong positive correlation was found compared with manual annotations from expert radiologists. In a study of Ahn et al [25], a Convolutional Neural Network (CNN) was used to learn the characteristics of dense and fatty tissues from 297 mammograms.…”

“…Furthermore, we plan to use a deep learning approach to find discriminant features and combine them with handcrafted features (e.g., local patterns from LQP operators). Although several studies [22][23][24][25][26][27][28][29] have investigated the use of deep learning in breast density classification, to the best of our knowledge, the separation was only based on two-or three-class classification (none of the deep learning based approaches has been applied to four-class classification). In fact, no study has been conducted combining non-handcrafted and handcrafted features in breast density classification.…”

Computational biology: deep learning

“…Neuron activations in the smallest layer can then be used as features for other machine learning methods; importantly, these are learned from data each time. This approach has been used to aid diagnoses for schizophrenia [66], brain tumors [67], lesions in the breast tissue [68,69], and atherosclerosis [70].…”