Invasive ductal carcinoma (IDC) comprises nearly 80% of all breast cancers. The detection of IDC is a necessary preprocessing step in determining the aggressiveness of the cancer, determining treatment protocols, and predicting patient outcomes, and is usually performed manually by an expert pathologist. Here, we describe a novel algorithm for automatically detecting IDC using semi-supervised conditional generative adversarial networks (cGANs). The framework is simple and effective at improving scores on a range of metrics over a baseline CNN.
翻译:侵入性肺癌(IDC)占所有乳腺癌的近80%,检测IDC是确定癌症攻击性、确定治疗规程和预测患者结果的必要预处理步骤,通常由专家病理学家手动进行。这里我们描述一种使用半监督的有条件对抗网络(cGANs)自动检测 IDC的新型算法。这个框架简单而有效地改善了有线电视新闻网基线线上的一系列计量的得分。