Dual-View Pyramid Pooling in Deep Neural Networks for Improved Medical Image Classification and Confidence Calibration

Spatial pooling (SP) and cross-channel pooling (CCP) operators have been applied to aggregate spatial features and pixel-wise features from feature maps in deep neural networks (DNNs), respectively. Their main goal is to reduce computation and memory overhead without visibly weakening the performance of DNNs. However, SP often faces the problem of losing the subtle feature representations, while CCP has a high possibility of ignoring salient feature representations, which may lead to both miscalibration of confidence issues and suboptimal medical classification results. To address these problems, we propose a novel dual-view framework, the first to systematically investigate the relative roles of SP and CCP by analyzing the difference between spatial features and pixel-wise features. Based on this framework, we propose a new pooling method, termed dual-view pyramid pooling (DVPP), to aggregate multi-scale dual-view features. DVPP aims to boost both medical image classification and confidence calibration performance by fully leveraging the merits of SP and CCP operators from a dual-axis perspective. Additionally, we discuss how to fulfill DVPP with five parameter-free implementations. Extensive experiments on six 2D/3D medical image classification tasks show that our DVPP surpasses state-of-the-art pooling methods in terms of medical image classification results and confidence calibration across different DNNs.

Efficient Pyramid Channel Attention Network for Pathological Myopia Detection

Pathological myopia (PM) is the leading ocular disease for impaired vision and blindness worldwide. The key to detecting PM as early as possible is to detect informative features in global and local lesion regions, such as fundus tessellation, atrophy and maculopathy. However, applying classical convolutional neural networks (CNNs) to efficiently highlight global and local lesion context information in feature maps is quite challenging. To tackle this issue, we aim to fully leverage the potential of global and local lesion information with attention module design. Based on this, we propose an efficient pyramid channel attention (EPCA) module, which dynamically explores the relative importance of global and local lesion context information in feature maps. Then we combine the EPCA module with the backbone network to construct EPCA-Net for automatic PM detection based on fundus images. In addition, we construct a PM dataset termed PM-fundus by collecting fundus images of PM from publicly available datasets (e.g., the PALM dataset and ODIR dataset). The comprehensive experiments are conducted on three datasets, demonstrating that our EPCA-Net outperforms state-of-the-art methods in detecting PM. Furthermore, motivated by the recent pretraining-and-finetuning paradigm, we attempt to adapt pre-trained natural image models for PM detection by freezing them and treating the EPCA module and other attention modules as the adapters. The results show that our method with the pretraining-and-finetuning paradigm achieves competitive performance through comparisons to part of methods with traditional fine-tuning methods with fewer tunable parameters.