Breast cancer is the most prevalent invasive type of cancer among women. The mortality rate of the disease can be reduced considerably through timely prognosis and felicitous treatment planning, by utilizing the computer aided detection and diagnosis techniques. With the advent of whole slide image (WSI) scanners for digitizing the histopathological tissue samples, there is a drastic increase in the availability of digital histopathological images. However, these samples are often unlabeled and hence they need labeling to be done through manual annotations by domain experts and experienced pathologists. But this annotation process required for acquiring high quality large labeled training set for nuclear atypia scoring is a tedious, expensive and time consuming job. Active learning techniques have achieved widespread acceptance in reducing this human effort in annotating the data samples. In this paper, we explore the possibilities of active learning on nuclear pleomorphism scoring over a non-Euclidean framework, the Riemannian manifold. Active learning technique adopted for the cancer grading is in the batch-mode framework, that adaptively identifies the apt batch size along with the batch of instances to be queried, following a submodular optimization framework. Samples for annotation are selected considering the diversity and redundancy between the pair of samples, based on the kernelized Riemannian distance measures such as log-Euclidean metrics and the two Bregman divergences - Stein and Jeffrey divergences. Results of the adaptive Batch Mode Active Learning on the Riemannian metric show a superior performance when compared with the state-of-the-art techniques for breast cancer nuclear pleomorphism scoring, as it makes use of the information from the unlabeled samples.

乳腺癌是女性中最普遍的浸润性癌症。通过使用计算机辅助的检测和诊断技术，可以通过及时的预后和适当的治疗计划来大大降低疾病的死亡率。随着用于对组织病理学组织样本进行数字化的全幻灯片图像（WSI）扫描仪的出现，数字组织病理学图像的可用性急剧增加。但是，这些样本通常没有标签，因此需要通过领域专家和经验丰富的病理学家的手动注释来完成标签。但是，获取高质量的大型标签训练集以进行核非典型性评分所需的这种注释过程是一项繁琐，昂贵且耗时的工作。主动学习技术已获得广泛的接受，可以减少人工注释数据样本的工作量。在本文中，我们探索了在非欧氏框架（黎曼流形）上主动学习核多态评分的可能性。用于癌症分级的主动学习技术是在批处理模式框架中的，它遵循子模块优化框架，自适应地识别适当的批处理大小以及要查询的实例批处理。基于核对的黎曼距离测度（例如对数欧几里得度量和两个Bregman散度-Stein和Jeffrey散度），考虑样本对之间的多样性和冗余性，选择用于注释的样本。