MONICA：长尾医学图像分类基准测试
MONICA: Benchmarking on Long-tailed Medical Image Classification
AI总结：
本文提出统一开源代码库MONICA，集成30多种长尾学习方法并在12个医学数据集上评估，为长尾医学图像分类提供可复现的基准与实用指导。
摘要：
长尾学习被认为是数据不平衡学习中极具挑战性的问题，其目标是从大量遵循长尾类别分布的图像中训练出具有良好泛化能力的模型。在医学领域，许多诊断影像检查（如皮肤镜检查和胸部X光摄影）会产生复杂临床发现的长尾分布。近年来，医学图像分析中的长尾学习受到了广泛关注。然而，该领域目前缺乏统一、严格制定且全面的基准，这往往导致不公平的比较和不确定的结论。为了帮助社区改进评估并推动发展，我们构建了一个统一、结构良好的代码库，名为Medical OpeN-source Long-taIled ClassifiCAtion（MONICA），该代码库实现了相关领域开发的30多种方法，并在涵盖6个医学领域的12个长尾医学数据集上进行了评估。我们的工作为该领域提供了有价值的实践指导和见解，对内置最先进方法中各个组件的有效性进行了详细分析和讨论。我们希望该代码库能作为一个全面且可复现的基准，促进长尾医学图像学习的进一步发展。该代码库已在https://github.com/PyJulie/MONICA上公开提供。
英文摘要：
Long-tailed learning is considered to be an extremely challenging problem in data imbalance learning. It aims to train well-generalized models from a large number of images that follow a long-tailed class distribution. In the medical field, many diagnostic imaging exams such as dermoscopy and chest radiography yield a long-tailed distribution of complex clinical findings. Recently, long-tailed learning in medical image analysis has garnered significant attention. However, the field currently lacks a unified, strictly formulated, and comprehensive benchmark, which often leads to unfair comparisons and inconclusive results. To help the community improve the evaluation and advance, we build a unified, well-structured codebase called Medical OpeN-source Long-taIled ClassifiCAtion (MONICA), which implements over 30 methods developed in relevant fields and evaluated on 12 long-tailed medical datasets covering 6 medical domains. Our work provides valuable practical guidance and insights for the field, offering detailed analysis and discussion on the effectiveness of individual components within the inbuilt state-of-the-art methodologies. We hope this codebase serves as a comprehensive and reproducible benchmark, encouraging further advancements in long-tailed medical image learning. The codebase is publicly available on https://github.com/PyJulie/MONICA.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
