轻量级模型CASCRNet提升胶囊内镜多病种分类准确率
CASCRNet: An Atrous Spatial Pyramid Pooling and Shared Channel Residual based Network for Capsule Endoscopy
- 采用共享通道残差与空洞金字塔池化结构，兼顾效率与特征提取
- 在复杂不均衡数据上实现78.5%的F1分数和98.3%的平均AUC
- 适合医疗图像分类场景，尤其适用于资源受限的嵌入式部署
本文针对胶囊内镜视觉挑战赛2024的数据集，提出一种名为CASCRNet（Capsule endoscopy-Aspp-SCR-Network）的轻量级新型模型，用于解决多类别疾病分类任务。由于数据集存在复杂性和类别不平衡问题，该模型引入共享通道残差（SCR）块与空洞空间金字塔池化（ASPP）块，有效提升特征表达能力。实验表明，所提模型在保持紧凑架构的同时，实现了78.5%的F1分数和98.3%的平均AUC，性能优于多种主流方法，具有良好的实用潜力。
原文摘要 · Abstract (English)
This manuscript summarizes work on the Capsule Vision Challenge 2024 by MISAHUB. To address the multi-class disease classification task, which is challenging due to the complexity and imbalance in the Capsule Vision challenge dataset, this paper proposes CASCRNet (Capsule endoscopy-Aspp-SCR-Network), a parameter-efficient and novel model that uses Shared Channel Residual (SCR) blocks and Atrous Spatial Pyramid Pooling (ASPP) blocks. Further, the performance of the proposed model is compared with other well-known approaches. The experimental results yield that proposed model provides better disease classification results. The proposed model was successful in classifying diseases with an F1 Score of 78.5% and a Mean AUC of 98.3%, which is promising given its compact architecture.
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