Multi-task Learning with Consistent Prediction for Efficient Breast Ultrasound Tumor Detection
Multi-task Learning with Consistent Prediction for Efficient Breast Ultrasound Tumor Detection
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DOI：
10.1109/bibm55620.2022.9995444
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Kaiwen Yang;Aiga Suzuki;Jiaxing Ye;H. Nosato;Ayumi Izumori;H. Sakanashi
中科院分区：
文献类型：
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作者：
Kaiwen Yang;Aiga Suzuki;Jiaxing Ye;H. Nosato;Ayumi Izumori;H. Sakanashi
分割和分类是乳腺超声图像肿瘤检测中高度相关的任务。最近的研究成功地将多任务学习应用于乳腺超声图像分析，以探索任务之间的相关性。然而，各个任务之间存在潜在的不一致，严重影响乳腺超声图像分析的整体性能。因此，本研究设计了一个一致性分支来协调分割和分类，并要求 0 优化。一致性分支表征各个特定任务模型的输出，以在训练期间保持一致性，从而生成高度一致的结果。具体来说，一致性分支输出一致性概率，同时确定两个任务预测的不一致类型。随后，基于每个样本不一致的预测行为，使用一致性概率来协调分割和分类损失权重，从而限制两个任务产生接近真实情况的一致预测。使用私人和公共乳腺超声图像数据集的评估表明，所提出的方法可以有效地纠正任务之间的不一致预测，以改进计算机化乳腺超声图像分析。
Segmentation and classification a re h ighly correlated tasks in tumor detection from breast ultrasound images. Recent studies have successfully applied multi-task learning to breast ultrasound image analysis to explore the correlation between tasks. However, there exists potential inconsistency between individual tasks that critically affect the overall performance of breast ultrasound image analysis. Therefore, this study designs a consistency branch for harmonizing the segmentation and classification t ask 0 ptimization. T he c onsistency b ranch characterizes the outputs of individual task-specific models to maintain consistency during training, thereby generating highly consistent results. Specifically, the consistency branch outputs a consistency probability while determining the inconsistency types predicted by both tasks. Subsequently, the segmentation and classification loss weights are reconciled using consistency probabilities based on the inconsistent prediction behavior for each sample, thus constraining the two tasks to produce consistent predictions close to the ground truth. The evaluation using private and public breast ultrasound image datasets indicates that the proposed method can effectively remedy the inconsistent predictions between tasks for improved computerized breast ultrasound image analysis.