论文标题
有限注释下的有丝分裂检测：一种联合学习方法
Mitosis Detection Under Limited Annotation: A Joint Learning Approach
论文作者
论文摘要
有丝分裂计数是乳腺癌肿瘤增殖的重要预后。基于深度学习的有丝分裂检测与病理学家相当，但需要大量标记的数据进行培训。我们提出了一个深层分类框架，以通过距离度量学习来利用类标签信息，通过软磁性损失和样品之间的空间分布信息来增强有丝分裂检测。我们还调查了稳步提供信息样本以增强学习的策略。提出的框架的功效是通过ICPR 2012和AMIDA 2013有丝分裂数据的评估确定的。与使用整个培训数据的最先进方法相比，我们的框架通过小型培训数据显着改善了检测，并实现了PAR或卓越性能。
Mitotic counting is a vital prognostic marker of tumor proliferation in breast cancer. Deep learning-based mitotic detection is on par with pathologists, but it requires large labeled data for training. We propose a deep classification framework for enhancing mitosis detection by leveraging class label information, via softmax loss, and spatial distribution information among samples, via distance metric learning. We also investigate strategies towards steadily providing informative samples to boost the learning. The efficacy of the proposed framework is established through evaluation on ICPR 2012 and AMIDA 2013 mitotic data. Our framework significantly improves the detection with small training data and achieves on par or superior performance compared to state-of-the-art methods for using the entire training data.
