用统一框架联合分割分类癌组织和细胞核，提升皮肤癌诊断精度。
A Multi-Stage Auto-Context Deep Learning Framework for Tissue and Nuclei Segmentation and Classification in H&E-Stained Histological Images of Advanced Melanoma
- 分阶段融合组织与细胞核信息的自上下文深度学习架构
- 在公开挑战赛中取得组织分割73.40% Dice、细胞核分类63.48% F1的顶尖成绩
- 适用于病理图像分析、精准医疗研究者参考
黑色素瘤是全球发病率持续上升的致命性皮肤癌。通过定位与分类组织及细胞核来分析苏木精-伊红染色的组织学图像，被视为临床诊断与治疗决策的金标准。尽管已有多种自动化分析方法提出，但多数将组织分析与细胞核分析分开处理，可能影响效果。本文基于PUMA挑战赛数据集，提出一种新型多阶段深度学习框架，结合自上下文思想，在统一模型中融合组织与细胞核信息，实现组织与细胞核的联合分割与分类。通过预训练与后处理优化，该方法在挑战赛中分别获得组织分割任务第二名（平均微Dice为73.40%）和细胞核分类任务第一名（总核F1得分为63.48%）。通过全面消融实验与外部数据集验证，证明了各模块有效性及模型泛化能力。代码已开源：https://github.com/NimaTorbati/PumaSubmit。
原文摘要 · Abstract (English)
Melanoma is the most lethal form of skin cancer, with an increasing incidence rate worldwide. Analyzing histological images of melanoma by localizing and classifying tissues and cell nuclei is considered the gold standard method for diagnosis and treatment options for patients. While many computerized approaches have been proposed for automatic analysis, most perform tissue-based analysis and nuclei (cell)-based analysis as separate tasks, which might be suboptimal. In this work, using the PUMA challenge dataset, we propose a novel multi-stage deep learning approach by combining tissue and nuclei information in a unified framework based on the auto-context concept to perform segmentation and classification in histological images of melanoma. Through pre-training and further post-processing, our approach achieved second and first place rankings in the PUMA challenge, with average micro Dice tissue score and summed nuclei F1-score of 73.40% for Track 1 and 63.48% for Track 2, respectively. Furthermore, through a comprehensive ablation study and additional evaluation on an external dataset, we demonstrated the effectiveness of the framework components as well as the generalization capabilities of the proposed approach. Our implementation for training and testing is available at: https://github.com/NimaTorbati/PumaSubmit
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