Although explainability is essential in the clinical diagnosis, most deep
learning models still function as black boxes without elucidating their
decision-making process. In this study, we investigate the explainable model
development that can mimic the decision-making process of human experts by
fusing the domain knowledge of explicit diagnostic criteria. We introduce a
simple yet effective framework, Explicd, towards Explainable language-informed
criteria-based diagnosis. Explicd initiates its process by querying domain
knowledge from either large language models (LLMs) or human experts to
establish diagnostic criteria across various concept axes (e.g., color, shape,
texture, or specific patterns of diseases). By leveraging a pretrained
vision-language model, Explicd injects these criteria into the embedding space
as knowledge anchors, thereby facilitating the learning of corresponding visual
concepts within medical images. The final diagnostic outcome is determined
based on the similarity scores between the encoded visual concepts and the
textual criteria embeddings. Through extensive evaluation of five medical image
classification benchmarks, Explicd has demonstrated its inherent explainability
and extends to improve classification performance compared to traditional
black-box models.

通过融合领域知识与大型语言模型，Explicd 提出了一种可解释的、以诊断标准为基础的语言辅助诊断框架，不仅模拟了人类专家的决策过程，还提高了医学图像分类性能。

将人类知识与视觉概念对齐，实现可解释的医学图像分类

Aligning Human Knowledge with Visual Concepts Towards Explainable  Medical Image Classification

Problem gambling is a major public health concern and is associated with
profound psychological distress and economic problems. There are numerous
gambling communities on the internet where users exchange information about
games, gambling tactics, as well as gambling-related problems. Individuals
exhibiting higher levels of problem gambling engage more in such communities.
Online gambling communities may provide insights into problem-gambling
behaviour. Using data scraped from a major German gambling discussion board, we
fine-tuned a large language model, specifically a Bidirectional Encoder
Representations from Transformers (BERT) model, to predict signs of
problem-gambling from forum posts. Training data were generated by manual
annotation and by taking into account diagnostic criteria and gambling-related
cognitive distortions. Using k-fold cross-validation, our models achieved a
precision of 0.95 and F1 score of 0.71, demonstrating that satisfactory
classification performance can be achieved by generating high-quality training
material through manual annotation based on diagnostic criteria. The current
study confirms that a BERT-based model can be reliably used on small data sets
and to detect signatures of problem gambling in online communication data. Such
computational approaches may have potential for the detection of changes in
problem-gambling prevalence among online users.

使用德国一个主要赌博讨论论坛上的数据，通过对论坛帖子进行 BERT 模型的调整，训练结果显示，通过基于诊断标准进行手工注释生成高质量训练材料，可以在小数据集上可靠地使用 BERT 模型检测在线交流数据中的问题赌博迹象，此类计算方法可能有助于检测在线用户中问题赌博患病率的变化。