解决胶囊内镜视频异常分类数据不平衡问题，提升小样本类识别效果。
Capsule Vision Challenge 2024: Multi-Class Abnormality Classification for Video Capsule Endoscopy
- 分层增强策略+渐进式训练任务，缓解少数类样本不足。
- 在Kaggle平台验证，使用ResNet50与自研ViT-CNN混合模型。
- 适合医疗图像分析、小样本异常检测方向的研究者参考。
本研究提出一种用于视频胶囊内镜（VCE）图像中异常分类的模型构建方法。针对数据不平衡问题，采用albumentations库实施分层增强策略，提升少数类样本代表性；同时通过渐进式训练任务设计，使模型先区分正常与异常，再根据数据可用性逐步引入更具体的类别。整个流程基于PyTorch实现，采用灵活架构以适应不同分类复杂度。实验在Kaggle平台上进行，使用ResNet50和自研ViT-CNN混合模型进行训练，验证了该方法在VCE异常分类中的可扩展性。
原文摘要 · Abstract (English)
This study presents an approach to developing a model for classifying abnormalities in video capsule endoscopy (VCE) frames. Given the challenges of data imbalance, we implemented a tiered augmentation strategy using the albumentations library to enhance minority class representation. Additionally, we addressed learning complexities by progressively structuring training tasks, allowing the model to differentiate between normal and abnormal cases and then gradually adding more specific classes based on data availability. Our pipeline, developed in PyTorch, employs a flexible architecture enabling seamless adjustments to classification complexity. We tested our approach using ResNet50 and a custom ViT-CNN hybrid model, with training conducted on the Kaggle platform. This work demonstrates a scalable approach to abnormality classification in VCE.
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