通过优化高分辨率病理图像推理，显著提升模型诊断性能。
Enhanced Diagnostic Performance via Large-Resolution Inference Optimization for Pathology Foundation Models
- 用空间感知块稀疏注意力，减少无效计算
- 推理时内存和时间开销降低，分类准确率最高提升7.67%
- 适合需要高精度病理分析的研究者和临床应用
尽管许多病理学基础模型在感兴趣区域（ROI）分类和分割任务中表现优异，但受限于固定输入尺寸（如224×224），在处理涵盖数千倍分辨率的全幻灯片图像（WSI）时效率低下。直接扩大输入会导致显存爆炸，而下采样则改变像素/微米比例，丢失关键形态细节。为此，我们提出一种时空高效推理策略：利用空间感知邻近块稀疏化注意力，并通过全局注意力得分过滤非信息性令牌。该方法在保持甚至提升下游性能的同时，大幅降低高分辨率WSI推理中的显存占用与运行时间，实现相同显卡预算下更高分辨率推理。实验表明，本方法在ROI分类上最高提升7.67%，分割性能保持相当。
原文摘要 · Abstract (English)
Despite their prominent performance on tasks such as ROI classification and segmentation, many pathology foundation models remain constrained by a specific input size e.g. 224 x 224, creating substantial inefficiencies when applied to whole-slide images (WSIs), which span thousands of resolutions. A naive strategy is to either enlarge inputs or downsample the WSIs. However, enlarging inputs results in prohibitive GPU memory consumption, while downsampling alters the microns-per-pixel resolution and obscures critical morphological details. To overcome these limitations, we propose an space- and time- efficient inference strategy that sparsifies attention using spatially aware neighboring blocks and filters out non-informative tokens through global attention scores. This design substantially reduces GPU memory and runtime during high-resolution WSI inference while preserving and even improving the downstream performance, enabling inference at higher resolutions under the same GPU budget. The experimental results show that our method can achieves up to an 7.67% improvement in the ROI classification and compatible results in segmentation.
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