Department of Electrical and Computer Engineering, University of British Columbia, Canada.
Department of Electrical and Computer Engineering, University of British Columbia, Canada.
Comput Biol Med. 2024 Nov;182:109179. doi: 10.1016/j.compbiomed.2024.109179. Epub 2024 Sep 25.
Sesamoiditis is a common equine disease with varying severity, leading to increased injury risks and performance degradation in horses. Accurate grading of sesamoiditis is crucial for effective treatment. Although deep learning-based approaches for grading sesamoiditis show promise, they remain underexplored and often lack clinical interpretability. To address this issue, we propose a novel, clinically interpretable multi-task learning model that integrates clinical knowledge with machine learning. The proposed model employs a dual-branch decoder to simultaneously perform sesamoiditis grading and vascular channel segmentation. Feature fusion is utilized to transfer knowledge between these tasks, enabling the identification of subtle radiographic variations. Additionally, our model generates a diagnostic report that, along with the vascular channel mask, serves as an explanation of the model's grading decisions, thereby increasing the transparency of the decision-making process. We validate our model on two datasets, demonstrating its superior performance compared to state-of-the-art models in terms of accuracy and generalization. This study provides a foundational framework for the interpretable grading of similar diseases.
籽骨炎是一种常见的马科动物疾病，其严重程度不一，会增加马匹受伤的风险，并降低其性能。准确分级籽骨炎对于有效治疗至关重要。尽管基于深度学习的籽骨炎分级方法具有很大的潜力，但它们仍未得到充分探索，且通常缺乏临床可解释性。为了解决这个问题，我们提出了一种新颖的、具有临床解释能力的多任务学习模型，该模型将临床知识与机器学习相结合。所提出的模型采用双分支解码器，同时进行籽骨炎分级和血管通道分割。特征融合用于在这些任务之间传递知识，从而能够识别细微的射线照相变化。此外，我们的模型生成一份诊断报告，该报告与血管通道掩模一起，作为模型分级决策的解释，从而增加决策过程的透明度。我们在两个数据集上验证了我们的模型，与最先进的模型相比，在准确性和泛化能力方面，该模型具有更优的性能。本研究为类似疾病的可解释分级提供了一个基础框架。
