论文标题
深层转移学习结构直肠癌组织学中的质地分类
Deep Transfer Learning for Texture Classification in Colorectal Cancer Histology
论文作者
论文摘要
组织或组织病理学的显微镜检查是检测结直肠癌的诊断程序之一。参与此类检查的病理学家通常会根据纹理分析鉴定组织类型，尤其是侧重于肿瘤疾病比率。在这项工作中，我们使用深层转移学习在结直肠癌组织学样本中的组织分类任务自动化。我们使用一环政策使用判别性微调，并应用结构性的颜色归一化来提高我们的结果。我们还提供了深层神经网络关于纹理分类的决定的视觉解释。通过达到96.2％的最新测试准确性，我们还开始使用称为Squeezenet的部署友好体系结构来进行内存限制的硬件。
Microscopic examination of tissues or histopathology is one of the diagnostic procedures for detecting colorectal cancer. The pathologist involved in such an examination usually identifies tissue type based on texture analysis, especially focusing on tumour-stroma ratio. In this work, we automate the task of tissue classification within colorectal cancer histology samples using deep transfer learning. We use discriminative fine-tuning with one-cycle-policy and apply structure-preserving colour normalization to boost our results. We also provide visual explanations of the deep neural network's decision on texture classification. With achieving state-of-the-art test accuracy of 96.2% we also embark on using deployment friendly architecture called SqueezeNet for memory-limited hardware.
