The significant success of deep learning has sparked interest in its application in medical diagnosis. Some deep learning models have achieved expert-level accuracy on some medical datasets, but these models are rarely used in clinical practice due to the lack of interpretability. Therefore, the research topic of explainable artificial intelligence (XAI) has emerged to make the reasoning process of the model transparent and interpretable. In this case, we applied interpretable artificial intelligence to dermatoscopy image diagnosis for the first time. Specifically, we use an interpretable prototype network for dermoscopy image diagnosis. To solve the problem of weak generalization performance of a single network, we propose to construct a new prototype network using the dual-path network. Besides, we propose a new gate similarity calculation method to reduce the activation of low-similarity regions, thereby reducing the generation of inaccurate prototypes and improving the diagnostic ability of the model. We conducted experiments on the dermoscopy datasets HAM10000 to test the model and compare it with other baseline models. Experimental results show that DP-ProtoNet has made improvements in accuracy while preserving the interpretability of the model.

Biomedical commonsense question answering is a challenging learning task that aims to give correct answers to biomedical commonsense questions. Many works have used rule-based or deep learning approachs to accomplish this task. Recently, an extensive research path is that pre-trained language models combined with graph neural networks (GNNs) to improve the accuracy of biomedical commonsense question answering. However, GNN is prone to the over-smoothing problem, causing the models to lose the ability to reason. In order to alleviate the over-smoothing problem and improve the inference ability for biomedical commonsense question answering, we propose a new end-to-end model named BiomGIN. In BiomGIN, we introduce the Graph Optimal Transport Networks (GOTNet) to use node-centroid attention to capture non-local messages in the knowledge graph, which alleviates the model over-smoothing problem. In addition, we design a question parsing module based on Transformer to generate linguistic instructions, which enhances the inference capability of the GNN. Finally, we evaluated our model on MedQA-USMLE dataset to compare with other baseline models. The experimental results demonstrate the method proposed in this paper achieves state-of-the-art results.

Transcriptome prediction from genetic variation data is an important task in the privacy-preserving and biometrics field, which can better protect genomic data and achieve biometric recognition through transcriptome. Many transcriptome prediction methods have achieved good accuracy from genetic variation data. However, these traditional transcriptome prediction methods have the problems of linear assumption, overfitting, expose personal privacy, and extensive manual optimization. To solve these shortcomings, we propose an attention-based transcriptome prediction model from genetic variation named RATPM that improves the accuracy of transcriptome prediction and protects participant genomic data. In RATPM, we introduce and improve the deep learning model with multi-head self-attention into the transcriptome prediction stage of Predixcan, which uncovers the non-linear relationship between genetic variation and transcriptome. Moreover, we introduce a residual attention module to generate attention-aware features and extract more accurate features at different levels from genetic variation. Furthermore, we introduce the BERT pre-training module to encode genetic variation fully utilizing their contextual information. Our research enables scientific institutions to publish only predicted transcriptomic data for biometric purposes, thus protecting the genomic information of the subjects. Finally, we evaluated our model on the 1000 Genomes and Geuvadis projects datasets to compare with other baseline models.