探索使用U-Net掩码自编码器和EfficientNet-B7进行自监督学习以改进视频胶囊内镜中胃肠异常分类
Exploring Self-Supervised Learning with U-Net Masked Autoencoders and EfficientNet-B7 for Improved Gastrointestinal Abnormality Classification in Video Capsule Endoscopy
AI总结：
本文提出结合U-Net掩码自编码器与EfficientNet-B7的自监督学习框架，用于改进视频胶囊内镜中胃肠异常分类，通过特征融合解决数据不平衡问题，验证了方法在准确率上的优势。
摘要：
视频胶囊内镜（VCE）因其非侵入性和高分辨率成像能力成为胃肠疾病诊断的不可或缺工具，但单次检查生成的数据量巨大，手动检查劳动强度大且易受观察者差异影响。深度学习自动化分析具有前景，但其效果常受限于数据不平衡和标注数据成本高。本文提出结合U-Net基于掩码自编码器的自监督学习与EfficientNet-B7的监督特征提取的新型框架，用于多类异常分类。U-Net模型首先通过高斯噪声去除和掩码重建进行自监督训练，学习鲁棒的视觉表征。学习到的编码器特征与EfficientNet-B7特征融合，形成丰富且具有区分性的表示。在包含十个异常类别和主要正常类的Capsule Vision 2024挑战数据集上评估，实验结果表明，所提融合框架在验证准确率上达到94%，优于单独架构和基于注意力的融合变体。研究强调了自监督表征学习和特征融合在解决类别不平衡和提升真实世界医学影像诊断准确性方面的有效性。
英文摘要：
Video Capsule Endoscopy (VCE) has become an indispensable diagnostic tool for gastrointestinal (GI) disorders due to its non-invasive nature and ability to capture high-resolution images of the small intestine. However, the enormous volume of data generated during a single procedure makes manual inspection labor-intensive, time-consuming, and prone to inter-observer variability. Automated analysis using deep learning offers a promising solution, but its effectiveness is often limited by data imbalance and the high cost of labeled medical data. In this work, we propose a novel framework that combines self-supervised learning through a U-Net-based masked autoencoder with supervised feature extraction using EfficientNet-B7 for multi-class abnormality classification in VCE images. The U-Net model is first trained in a self-supervised manner using Gaussian noise removal and masked reconstruction to learn robust visual representations without requiring annotations. The learned encoder features are then fused with EfficientNet-B7 features to form a rich, discriminative representation for classification. We evaluate our approach on the Capsule Vision 2024 Challenge dataset consisting of ten abnormality classes and a dominant normal class. Experimental results demonstrate that the proposed fusion framework achieves a validation accuracy of 94\%, outperforming standalone architectures and attention-based fusion variants. The study highlights the effectiveness of self-supervised representation learning and feature fusion in addressing class imbalance and improving diagnostic accuracy in real-world medical imaging scenarios.
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