用统计方法自动识别病理图像中的癌变区域，无需精确划分边界。
Quantifying Cancer Likeness: A Statistical Approach for Pathological Image Diagnosis
- 基于信息论区分癌变与正常特征，正负值对应不同组织状态
- 在癌症分类任务中AUC达0.95以上，性能优异
- 无需精细标注边界，减轻病理医生重复工作负担
本文提出一种新的统计方法，用于自动识别病理图像中的癌变区域。该方法基于循证医学的统计理论，核心技术包括：基于信息论的图像特征分类机制——癌变特征取正值，正常特征取负值；以及判断特征空间分布的计算方法。通过识别分类信息内容为正的区域，判定为癌变区域。该方法在癌症分类任务中取得AUC 0.95或更高的表现。此外，该方法无需精确划定癌变与正常组织的分界线，有效缓解病理科医生因反复协商一致而产生的重复性工作压力。
原文摘要 · Abstract (English)
In this paper, we present a new statistical approach to automatically identify cancer regions in pathological images. The proposed method is built from statistical theory in line with evidence-based medicine. The two core technologies are the classification information of image features, which was introduced based on information theory and which cancer features take positive values, normal features take negative values, and the calculation technique for determining their spatial distribution. This method then estimates areas where the classification information content shows a positive value as cancer areas in the pathological image. The method achieves AUCs of 0.95 or higher in cancer classification tasks. In addition, the proposed method has the practical advantage of not requiring a precise demarcation line between cancer and normal. This frees pathologists from the monotonous and tedious work of building consensus with other pathologists.
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