Deep learning models used for medical image classification tasks are often constrained by the limited amount of training data along with severe class imbalance. Despite these problems, models should be explainable to enable human trust in the models' decisions to ensure wider adoption in high-risk situations. In this paper, we propose PRECISe, an explainable-by-design model meticulously constructed to concurrently address all three challenges. Evaluation on 2 imbalanced medical image datasets reveals that PRECISe outperforms the current state-of-the-art methods on data efficient generalization to minority classes, achieving an accuracy of ~87% in detecting pneumonia in chest x-rays upon training on <60 images only. Additionally, a case study is presented to highlight the model's ability to produce easily interpretable predictions, reinforcing its practical utility and reliability for medical imaging tasks.

本研究针对医疗图像分类中训练数据有限和类别不平衡的问题，提出了一种称为PRECISe的可解释模型，该模型旨在同时解决这三个挑战。 evaluations on two imbalanced medical image datasets 显示，PRECISe在针对少数类进行数据高效泛化方面优于现有的前沿方法，其在仅使用少于60张图像训练时，检测肺炎的准确率达到了约87%。

PRECISe：在不平衡和稀缺数据环境下的可解释分类原型保留