两阶段检测框架提升癌细胞分裂定位准确率
A Two-Stage Strategy for Mitosis Detection Using Improved YOLO11x Proposals and ConvNeXt Classification
- 先用改进YOLO11x生成候选框，再用ConvNeXt分类器过滤假阳性
- 在融合数据集上达到0.882的F1分数，精度提升至0.839
- 适合病理图像中复杂背景下的微小目标检测任务
MIDOG 2025 Track 1要求在包含非肿瘤、炎症和坏死区域的全切片图像（WSIs）中检测癌细胞分裂。由于上下文复杂且存在潜在伪影，常出现假阳性和假阴性，导致检测F1分数下降。为此，我们提出两阶段框架：首先使用集成EMA注意力与LSConv的改进YOLO11x生成候选框，采用低置信度阈值以最大化召回；随后用ConvNeXt-Tiny分类器剔除假阳性，提升精度。在MIDOG++、MITOS_WSI_CCMCT和MITOS_WSI_CMC的融合数据集上，该框架实现0.882的F1分数，较单阶段YOLO11x基线提升0.035，其中精度从0.762升至0.839，召回率保持相当。在MIDOG 2025 Track 1预赛测试集上，算法F1分数为0.7587。代码已开源。
原文摘要 · Abstract (English)
MIDOG 2025 Track 1 requires mitosis detection in whole-slideimages (WSIs) containing non-tumor, inflamed, and necrotic re-gions. Due to the complicated and heterogeneous context, aswell as possible artifacts, there are often false positives and falsenegatives, thus degrading the detection F1-score. To addressthis problem, we propose a two-stage framework. Firstly, an im-proved YOLO11x, integrated with EMA attention and LSConv,is employed to generate mitosis candidates. We use a low confi-dence threshold to generate as many proposals as possible, en-suring the detection recall. Then, a ConvNeXt-Tiny classifieris employed to filter out the false positives, ensuring the detec-tion precision. Consequently, the proposed two-stage frame-work can generate a high detection F1-score. Evaluated on afused dataset comprising MIDOG++, MITOS_WSI_CCMCT,and MITOS_WSI_CMC, our framework achieves an F1-scoreof 0.882, which is 0.035 higher than the single-stage YOLO11xbaseline. This performance gain is produced by a significantprecision improvement, from 0.762 to 0.839, and a comparablerecall. On the MIDOG 2025 Track 1 preliminary test set, thealgorithm scores an F1 score of 0.7587. The code is available athttps://github.com/xxiao0304/MIDOG-2025-Track-1-of-SZTU.
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