arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新，休息一下吧，周末愉快~~
arXiv 2307.02000eess.IVcs.CVcs.LG

从超声中蒸馏缺失模态知识以用于基于磁共振图像的子宫内膜异位症诊断

Distilling Missing Modality Knowledge from Ultrasound for Endometriosis Diagnosis with Magnetic Resonance Images

Australian Institute for Machine Learning, University of Adelaide（阿德莱德大学澳大利亚机器学习研究所）；Flinders Health and Medical Research Institute, Flinders University（弗林德斯大学弗林德斯健康与医学研究所）；Robinson Research Institute, University of Adelaide（阿德莱德大学罗宾逊研究所）；Centre for Vision, Speech and Signal Processing, University of Surrey（萨里大学视觉、语音与信号处理中心）

Yuan Zhang, Hu Wang, David Butler, Minh-Son To, Jodie Avery, M Louise Hull, Gustavo Carneiro

备注：
This paper is accepted by 2023 IEEE 20th International Symposium on Biomedical Imaging(ISBI 2023)
阅读 PDF
arXiv 页面 ↗HTML 原文 ↗

AI总结：

针对子宫内膜异位症POD闭塞检测中MRI难度大、患者常缺TVUS数据的问题，提出知识蒸馏算法，用未配对TVUS数据的教师模型知识提升MRI学生模型的检测准确率。

摘要：

子宫内膜异位症是一种常见的慢性妇科疾病，具有多种特征，包括道格拉斯窝（POD）闭塞，可通过经阴道妇科超声（TVUS）扫描和磁共振成像（MRI）进行诊断。TVUS和MRI是互补的非侵入性子宫内膜异位症诊断成像技术，但患者通常不会同时接受两种模态的扫描，而且从MRI中检测POD闭塞通常比从TVUS中检测更具挑战性。为了缓解这种分类不平衡问题，本文提出了一种知识蒸馏训练算法，利用未配对的TVUS数据的检测结果来改进基于MRI的POD闭塞检测。具体而言，该算法预训练一个教师模型，用于从TVUS数据中检测POD闭塞，同时使用大量未标记的盆腔3D MRI体积数据，通过3D掩码自编码器预训练学生模型。接下来，我们从教师TVUS POD闭塞检测器中蒸馏知识，通过最小化回归损失来训练学生MRI模型，该损失利用未配对的TVUS和MRI数据使学生模型的输出逼近教师模型的输出。在我们包含TVUS和MRI数据的子宫内膜异位症数据集上的实验结果表明，该方法可有效提高基于MRI的POD检测准确率。

英文摘要：

Endometriosis is a common chronic gynecological disorder that has many characteristics, including the pouch of Douglas (POD) obliteration, which can be diagnosed using Transvaginal gynecological ultrasound (TVUS) scans and magnetic resonance imaging (MRI). TVUS and MRI are complementary non-invasive endometriosis diagnosis imaging techniques, but patients are usually not scanned using both modalities and, it is generally more challenging to detect POD obliteration from MRI than TVUS. To mitigate this classification imbalance, we propose in this paper a knowledge distillation training algorithm to improve the POD obliteration detection from MRI by leveraging the detection results from unpaired TVUS data. More specifically, our algorithm pre-trains a teacher model to detect POD obliteration from TVUS data, and it also pre-trains a student model with 3D masked auto-encoder using a large amount of unlabelled pelvic 3D MRI volumes. Next, we distill the knowledge from the teacher TVUS POD obliteration detector to train the student MRI model by minimizing a regression loss that approximates the output of the student to the teacher using unpaired TVUS and MRI data. Experimental results on our endometriosis dataset containing TVUS and MRI data demonstrate the effectiveness of our method to improve the POD detection accuracy from MRI.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

↑