# PRECISe : Prototype-Reservation for Explainable Classification under Imbalanced and Scarce-Data Settings Source: [PRECISe : Prototype-Reservation for Explainable Classification under Imbalanced and Scarce-Data Settings]() Collection index: [llms.txt]() Data updated: 2025-10-26T03:10:37.944Z Authors: Vaibhav Ganatra, Drishti Goel Published: 2024-08-11T12:05:32.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()