Breast lesion detection in mammograms based on unsupervised convolutional neural network
Mengze Chen, Zhili Chen, Adamu Abubakar Abba · 2023
To reduce the dependence of lesion detection algorithms on annotated data and fully leverage the advantages of medical image big data from healthy people, this paper proposes an unsupervised breast lesion detection model based on a parallel convolutional neural network. It treats breast lesion detection in mammograms as a one-class classification problem. The network comprises two parallel convolutional neural network branches. One branch utilizes normal mammographic images as training samples to extract the deep features of normal images and considersthe compactness measure of features as the training loss. Simultaneously, to prevent the lack of inter-class discrimination in features extracted from the first branch's sample learning, the other branch introduces a subset of the ImageNet dataset as training samples and employs the descriptive measure of features as the training loss. This ensures that the extracted features not only satisfy intra-class similarity but also maintain inter-class discrimination. ResNet50 is chosen by comparison as the backbone network for the parallel branch and further improved. Experiments are conducted using the INbreast and BCDR datasets, and the results indicate that the proposed unsupervised lesion detection method could achieve comparable performance with the supervised methods without requiring any lesion annotation data.