论文标题
组织学前列腺图像通过剩余U-NET的语义分割的格里森分级
Gleason Grading of Histology Prostate Images through Semantic Segmentation via Residual U-Net
论文作者
论文摘要
在全球，前列腺癌是影响男性的主要癌症之一。前列腺癌的最终诊断是基于病理学家对前列腺活检中格里森模式的目视检测。计算机辅助诊断系统允许通过计算机视觉算法描述和对组织中的癌性模式进行分类，以支持医生的任务。这项工作的方法论核心是一个U-NET卷积神经网络，用于修饰图像分割，并根据完整的格里森系统进行癌组织的残留块。该模型的表现优于其他知名体系结构，并在文献中以前图像级作品的级别上达到了像素级Cohen的二次Kappa，但也提供了模式的详细本地化。
Worldwide, prostate cancer is one of the main cancers affecting men. The final diagnosis of prostate cancer is based on the visual detection of Gleason patterns in prostate biopsy by pathologists. Computer-aided-diagnosis systems allow to delineate and classify the cancerous patterns in the tissue via computer-vision algorithms in order to support the physicians' task. The methodological core of this work is a U-Net convolutional neural network for image segmentation modified with residual blocks able to segment cancerous tissue according to the full Gleason system. This model outperforms other well-known architectures, and reaches a pixel-level Cohen's quadratic Kappa of 0.52, at the level of previous image-level works in the literature, but providing also a detailed localisation of the patterns.
