用于二分医学图像分割的启发性特征增强
Instructive Feature Enhancement for Dichotomous Medical Image Segmentation
AI总结：
针对二分医学图像分割中模型设计复杂且缺乏特征通道指引的问题，本文提出即插即用的IFE模块，依据局部曲率或全局信息熵增强纹理判别特征，并构建涵盖7种模态、26个解剖结构的55,023张图像数据集Cosmos55k，验证其可稳定提升多种经典网络。
摘要：
深度神经网络已被广泛应用于多种模态中许多解剖结构的二分医学图像分割（DMIS），并取得了可观性能。然而，现有网络往往依赖针对特定任务、繁重且复杂的设计来提高准确率。它们几乎没有指明哪些特征通道更有利于分割，这可能正是这些分割模型的性能和通用性受到阻碍的原因。在本研究中，我们提出一种启发性特征增强方法，即IFE，基于局部曲率或全局信息熵准则，自适应地选择具有丰富纹理线索和强判别性的特征通道，以增强原始特征。IFE即插即用，适用于多种DMIS任务，可促使模型关注纹理丰富的特征，这些特征对于模糊且具有挑战性的边界识别尤为重要，同时实现简洁性、通用性和一定的可解释性。为评估所提出的IFE，我们构建了首个大规模DMIS数据集Cosmos55k，其中包含来自7种模态和26个解剖结构的55,023张图像。大量实验表明，仅需轻微修改，IFE即可提升经典分割网络在不同解剖结构和模态上的性能。代码位于https://github.com/yezi-66/IFE。
英文摘要：
Deep neural networks have been widely applied in dichotomous medical image segmentation (DMIS) of many anatomical structures in several modalities, achieving promising performance. However, existing networks tend to struggle with task-specific, heavy and complex designs to improve accuracy. They made little instructions to which feature channels would be more beneficial for segmentation, and that may be why the performance and universality of these segmentation models are hindered. In this study, we propose an instructive feature enhancement approach, namely IFE, to adaptively select feature channels with rich texture cues and strong discriminability to enhance raw features based on local curvature or global information entropy criteria. Being plug-and-play and applicable for diverse DMIS tasks, IFE encourages the model to focus on texture-rich features which are especially important for the ambiguous and challenging boundary identification, simultaneously achieving simplicity, universality, and certain interpretability. To evaluate the proposed IFE, we constructed the first large-scale DMIS dataset Cosmos55k, which contains 55,023 images from 7 modalities and 26 anatomical structures. Extensive experiments show that IFE can improve the performance of classic segmentation networks across different anatomies and modalities with only slight modifications. Code is available at https://github.com/yezi-66/IFE
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。
