An Infantile Hemangioma Dataset IH-2021 and a Deep Learning based Recognition Method on it

Abstract

Infantile hemangioma(IH) is one of the most common skin and soft tissue tumors in children. From the appearance, it is very easy to be confused with vascular malformation. As a result, the misdiagnosis between them often occurs. To help doctors screen and identify hemangioma patients, we employed image recognition technology based on deep learning to recognize photos of patients. At first, we process and sort out the existing medical image to establish and release an infantile hemangioma dataset named IH-2021. Then we do classification experiments and obtain the baseline results on it. However, the number of medical images is usually rather small, directly leading to the overfitting of deep learning methods. To further improve the performance of image classification, we constructed a new neural network for image classification of infantile hemangioma, in which we introduced data augmentation approaches based on generative adversarial network and active learning. It has achieved better results on IH-2021 compared with other state-of-the-art models for this task.