Mitotic figure detection in histology images is a hard-to-define, yet clinically significant task, where labels are generated with pathologist interpretations and where there is no ``gold-standard'' independent ground-truth. However, it is well-established that these interpretation based labels are often unreliable, in part, due to differences in expertise levels and human subjectivity. In this paper, our goal is to shed light on the inherent uncertainty of mitosis labels and characterize the mitotic figure classification task in a human interpretable manner. We train a probabilistic diffusion model to synthesize patches of cell nuclei for a given mitosis label condition. Using this model, we can then generate a sequence of synthetic images that correspond to the same nucleus transitioning into the mitotic state. This allows us to identify different image features associated with mitosis, such as cytoplasm granularity, nuclear density, nuclear irregularity and high contrast between the nucleus and the cell body. Our approach offers a new tool for pathologists to interpret and communicate the features driving the decision to recognize a mitotic figure.

翻译：组织学图像中的核分裂象检测是一项难以界定但具有重要临床意义的任务，其标注依赖于病理学家的解读，且缺乏独立的"金标准"真值。然而，由于专业知识水平差异和人为主观性等因素，这些基于解读的标注往往不可靠。本文旨在揭示核分裂象标注中固有的不确定性，并以可解释的方式描述核分裂象分类任务。我们训练了一个概率扩散模型，用于在给定核分裂象标签条件下合成细胞核图像斑块。利用该模型，可生成对应同一细胞核向有丝分裂状态转变过程的合成图像序列。这使我们能够识别与有丝分裂相关的不同图像特征，包括细胞质颗粒度、核密度、核不规则性以及细胞核与胞体间的高对比度。本研究为病理学家理解和交流核分裂象识别决策的关键特征提供了新工具。