基于深度学习的结直肠息肉内镜图像分割和分类方法比较

陈健, 王珍妮, 夏开建, 王甘红, 刘罗杰, 徐晓丹

Comparative study on methods for colon polyp endoscopic image segmentation and classification based on deep learning

CHEN Jian, WANG Zhenni, XIA Kaijian, WANG Ganhong, LIU Luojie, XU Xiaodan

图3 4种分割模型在训练集上的性能指标随训练进程的变化 Note： A/B.Accuracy (A) and loss function (B) fluctuation of the Fast-SCNN model. C/D.Variations in accuracy (C) and loss function (D) in the DeepLabV3plus model. E/F. Segformer model's accuracy (E) and loss function (F) changes. G/H. KNet model's accuracy evolution (G) and loss function (H)alterations. All figures encapsulate the decoder segmentation accuracy (decode.acc_seg) and auxiliary classifier segmentation accuracy (aux.acc_seg).

Fig 3 Changes in performance metrics for four segmentation models in the training set throughout the training process