无需重训，用推荐系统为医学影像选最优模型
MedicalRec: Medical recommender system for image classification without retraining
- 基于Transformer构建医学影像模型推荐系统
- 在18个特征下达到75.5%的最高命中率@100
- 适合医疗AI从业者快速选型，节省试错成本
机器学习与深度学习虽大幅提升医疗诊断、治疗和管理效率，但其高算力消耗、能源浪费及碳排放问题日益突出。当前模型选择依赖试错，耗能且低效。本文提出MedicalRec——一个面向医学图像分类的模型推荐系统。研究收集3,000篇文献数据，构建公开数据集MedicalRec-Bench，包含超过5,000条记录，涵盖皮肤癌、肿瘤、伤口、乳腺癌及MRI分类等任务。该数据集按特征数量分为四类：MedicalRec I（5特征）、II（9特征）、III（11特征）和IV（18特征），因作者未报告全部特征，存在大量缺失值。基于Transformer的MedicalRec模型在该数据集及12个基线模型评估中表现优异，最大命中率@100达75.5%。代码与数据已开源：https://github.com/Ramin1Mousa/MedicalRec
原文摘要 · Abstract (English)
The emergence of machine learning and deep learning has revolutionized the efficiency of diagnostic, therapeutic, and administrative systems in healthcare. However, this rapid adoption has come at the cost of requiring significant computing power and energy consumption, as well as e-waste disposal and carbon emissions. One of the challenges of these models is choosing the right model for classification tasks. To this end, researchers attempt to identify the optimal model using their data through trial and error, which involves energy consumption and waste. The goal of this study is to develop a model-based recommender system for medical image classification. For this purpose, a data set was collected from 3,000 articles in the field of medical image classification. This dataset, publicly available under the name MedicalRec-Bench, contains over 5,000 records of models tested in various tasks, including Skin Cancer Classification, Tumour Classification, Wound Classification, Breast Cancer, and MRI classification. The dataset was evaluated in four different modes, depending on the number of features: MedicalRec I (5 features), MedicalRec II (9 features), MedicalRec III (11 features), and MedicalRec IV (18 features). Collecting all values for the features is challenging due to non-reporting by the authors; hence, the dataset contains significant amounts of missing values. The Medical Recommender System (MedicalRec) is a transformer-based model used for item recommendations in this study. This model achieved remarkable results in the evaluation on the dataset and in the evaluation with 12 base models. This model achieved a maximum HitRate@100 of 75.5%. The dataset and implementations are available through the GitHub link: https://github.com/Ramin1Mousa/MedicalRec
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