品質引導的半監督學習應用於醫學影像分割

摘要

訓練準確的醫學影像分割模型需要大量密集標註的資料，而取得此類資料既昂貴又耗時。半監督學習（SSL）可透過同時利用大量未標註資料與少量標註資料來減輕此問題。然而，多數現代半監督學習方法依賴於未標註資料的偽標籤，並通常透過模型信心或不確定性來評估其可靠度，此類評估方式具有自我參照性，且缺乏基於分割品質的明確依據。為此，我們提出一種以品質引導的半監督學習框架，透過訓練專用網路，從影像-遮罩對中估算分割品質。該品質預測器基於透過合成擾動所產生的可變品質遮罩進行訓練，並結合部分訓練過的分割模型所輸出的不完美結果，以此捕捉訓練過程中遇到的實際誤差模式。我們透過兩種互補機制將品質預測器整合至半監督學習中：品質感知正則化損失函數，以及基於品質的偽標籤樣本重新加權方案。我們證明，該方法可作為現有半監督學習框架的即插即用增強模組。在五個資料集與多種架構上進行的廣泛實驗顯示，相較於其他競爭性的半監督學習方法，本方法持續帶來改進，推動了半監督醫學影像分割的最新進展。

English

Training accurate medical image segmentation models requires large amounts of densely annotated data, which is costly and time-consuming to obtain. Semi-supervised learning (SSL) alleviates this by learning from both abundant unlabeled data and limited labeled data. However, most modern SSL methods rely on pseudolabels for unlabeled data, and typically assess their reliability through model confidence or uncertainty, measures that are self-referential and lack explicit grounding in segmentation quality. Instead, we propose a quality-guided SSL framework that trains a dedicated network to estimate segmentation quality from image-mask pairs. The predictor is trained on variable-quality masks generated through synthetic corruptions augmented with imperfect outputs from partially trained segmentation models, capturing realistic error patterns encountered during training. We integrate the quality predictor into SSL through two complementary mechanisms: a quality-aware regularization loss and a quality-based pseudolabel sample reweighting scheme. We show that our method serves as a drop-in enhancement to existing SSL frameworks. Extensive experiments across five datasets and multiple architectures demonstrate consistent improvements over competing SSL methods, advancing the state-of-the-art in semi-supervised medical image segmentation.