用单层反卷积提升医学图像分类分割精度，参数几乎不变
Med-IC: Fusing a Single Layer Involution with Convolutions for Enhanced Medical Image Classification and Segmentation
- 在CNN前加单层反卷积，增强空间特征提取能力
- 在多个医学图像数据集上显著提升分类与分割性能
- 仅用一层反卷积即超越多数现有方法，适合轻量级医疗应用
大多数医学图像，尤其是细胞类图像，具有相似特征，其形态多样，常在器官或细胞区域出现异常。传统卷积操作在跨空间区域提取视觉模式方面能力有限。反卷积作为卷积的逆操作，可弥补卷积在空间信息提取上的不足。本研究探究在卷积神经网络（CNN）架构前加入单层反卷积对医学图像分类与分割性能的影响。实验表明，该策略能显著提升性能，且仅引入极少额外参数。同时发现，过度使用反卷积层可能导致特定类型医学图像预测失准。结果表明，仅在CNN前添加一层反卷积，即可超越多数先前工作。
原文摘要 · Abstract (English)
The majority of medical images, especially those that resemble cells, have similar characteristics. These images, which occur in a variety of shapes, often show abnormalities in the organ or cell region. The convolution operation possesses a restricted capability to extract visual patterns across several spatial regions of an image. The involution process, which is the inverse operation of convolution, complements this inherent lack of spatial information extraction present in convolutions. In this study, we investigate how applying a single layer of involution prior to a convolutional neural network (CNN) architecture can significantly improve classification and segmentation performance, with a comparatively negligible amount of weight parameters. The study additionally shows how excessive use of involution layers might result in inaccurate predictions in a particular type of medical image. According to our findings from experiments, the strategy of adding only a single involution layer before a CNN-based model outperforms most of the previous works.
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