  Computer-aided diagnosis of skin diseases is an important tool. However, the
interpretability of computer-aided diagnosis is currently poor. Dermatologists
and patients cannot intuitively understand the learning and prediction process
of neural networks, which will lead to a decrease in the credibility of
computer-aided diagnosis. In addition, traditional methods need to be trained
using negative samples in order to predict the presence or absence of a lesion,
but medical data is often in short supply. In this paper, we propose a multiple
high-order attention interaction model (MHA-UNet) for use in a highly
explainable skin lesion segmentation task. MHA-UNet is able to obtain the
presence or absence of a lesion by explainable reasoning without the need for
training on negative samples. Specifically, we propose a high-order attention
interaction mechanism that introduces squeeze attention to a higher level for
feature attention. In addition, a multiple high-order attention interaction
(MHAblock) module is proposed by combining the different features of different
orders. For classifying the presence or absence of lesions, we conducted
classification experiments on several publicly available datasets in the
absence of negative samples, based on explainable reasoning about the
interaction of 5 attention orders of MHAblock. The highest positive detection
rate obtained from the experiments was 81.0% and the highest negative detection
rate was 83.5%. For segmentation experiments, comparison experiments of the
proposed method with 13 medical segmentation models and external validation
experiments with 8 state-of-the-art models in three public datasets and our
clinical dataset demonstrate the state-of-the-art performance of our model. The
code is available from https://github.com/wurenkai/MHA-UNet.
