School of Computer Science, Jiangxi University of Traditional Chinese Medicine, Nanchang, 330004, China.
State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China.
Sci Rep. 2024 Sep 20;14(1):21984. doi: 10.1038/s41598-024-70750-7.
The analysis and interpretation of cytopathological images are crucial in modern medical diagnostics. However, manually locating and identifying relevant cells from the vast amount of image data can be a daunting task. This challenge is particularly pronounced in developing countries where there may be a shortage of medical expertise to handle such tasks. The challenge of acquiring large amounts of high-quality labelled data remains, many researchers have begun to use semi-supervised learning methods to learn from unlabeled data. Although current semi-supervised learning models partially solve the issue of limited labelled data, they are inefficient in exploiting unlabeled samples. To address this, we introduce a new AI-assisted semi-supervised scheme, the Reliable-Unlabeled Semi-Supervised Segmentation (RU3S) model. This model integrates the ResUNet-SE-ASPP-Attention (RSAA) model, which includes the Squeeze-and-Excitation (SE) network, Atrous Spatial Pyramid Pooling (ASPP) structure, Attention module, and ResUNet architecture. Our model leverages unlabeled data effectively, improving accuracy significantly. A novel confidence filtering strategy is introduced to make better use of unlabeled samples, addressing the scarcity of labelled data. Experimental results show a 2.0% improvement in mIoU accuracy over the current state-of-the-art semi-supervised segmentation model ST, demonstrating our approach's effectiveness in solving this medical problem.
细胞病理学图像的分析和解释在现代医学诊断中至关重要。然而，从大量的图像数据中手动定位和识别相关细胞可能是一项艰巨的任务。在发展中国家，这种挑战尤为明显，因为可能缺乏医学专业知识来处理此类任务。获取大量高质量标记数据的挑战仍然存在，许多研究人员已经开始使用半监督学习方法从未标记的数据中学习。虽然当前的半监督学习模型部分解决了标记数据有限的问题，但它们在利用未标记样本方面效率低下。为了解决这个问题，我们引入了一种新的人工智能辅助半监督方案，即可靠无标记半监督分割（RU3S）模型。该模型集成了 ResUNet-SE-ASPP-Attention（RSAA）模型，其中包括 Squeeze-and-Excitation（SE）网络、Atrous Spatial Pyramid Pooling（ASPP）结构、注意力模块和 ResUNet 架构。我们的模型有效地利用了未标记的数据，显著提高了准确性。引入了一种新颖的置信度过滤策略，以更好地利用未标记样本，解决标记数据的稀缺问题。实验结果表明，与当前最先进的半监督分割模型 ST 相比，mIoU 准确性提高了 2.0%，证明了我们的方法在解决这一医学问题方面的有效性。
