Pathological Changes Discover Network: Discover the Pathological Changes of Perivascular Dermatitis by Semi-supervised Learning

Deep learning method becomes increasingly popular in many fields, especially in computer vision area, and it is gradually being applied in the medical image area to solve medical image problems such as lesion segmentation, nucleus detection and disease classification. However, training deep learning segment models requires a lot of accurate pixel-level segmentation medical image labels, which is very hard to obtain. Lacking accurate pixel-level segmentation labels is an important factor restricting the development of deep learning method in medical image processing. In this paper, we propose a perivascular dermatitis classification network based on semi-supervised learning which is trained by image-level supervision. Our network can accurately and effectively segment the pathological changes contributed to the disease classification while classifying perivascular dermatitis. We use U-Net as the pathological change discover module to segment the pathological changes, propose the restricted boundary loss to improve the accuracy of the segmentation area boundary and introduce the pathological changes guided module to guide the pathological changes discover module to generate pathological changes contribute to the classification task. We evaluate our network on the dataset of skin pathological images with image-level classification labels. With the image-level labels of the perivascular dermatitis pathological images and normal skin pathological images, our network is trained to discover the pathological changes and classify the skin pathological images. Experiments show that our network discovers the skin pathological changes by using image-level classification labels. The perivascular dermatitis classification accuracy, AUC and PR of our network is 0.8793, 0.8954 and 0.8041.