通过挖掘难样本提升医学图像分割性能，自监督训练更精准。
SelfMedHPM: Self Pre-training With Hard Patches Mining Masked Autoencoders For Medical Image Segmentation
- 自建难块挖掘机制，动态选择最难重建区域进行遮蔽训练。
- 在BTCV与SMWB数据集上分割精度优于现有方法。
- 适合需要高精度医学图像分割的临床研究与模型开发人员。
近年来，卷积神经网络（CNN）和变换器（transformers）在CT多器官分割任务中取得显著进展。然而基于掩码图像建模（MIM）的方法仍较为有限。现有方法虽已采用MAE进行腹部和全身多器官分割，但未充分识别最难重建的区域。为此，我们提出一种结合难块挖掘的自监督预训练框架SelfMedHPM，利用ViT在目标数据集上进行自预训练，并引入辅助损失预测器，根据预测的块损失动态确定下一轮遮蔽位置。该方法在腹部和全身多器官分割任务中均表现优异，在BTCV数据集（腹部）和SinoMed Whole Body（SMWB，全身）数据集上均超越多种对比方法。
原文摘要 · Abstract (English)
In recent years, deep learning methods such as convolutional neural network (CNN) and transformers have made significant progress in CT multi-organ segmentation. However, CT multi-organ segmentation methods based on masked image modeling (MIM) are very limited. There are already methods using MAE for CT multi-organ segmentation task, we believe that the existing methods do not identify the most difficult areas to reconstruct. To this end, we propose a MIM self-training framework with hard patches mining masked autoencoders for CT multi-organ segmentation tasks (selfMedHPM). The method performs ViT self-pretraining on the training set of the target data and introduces an auxiliary loss predictor, which first predicts the patch loss and determines the location of the next mask. SelfMedHPM implementation is better than various competitive methods in abdominal CT multi-organ segmentation and body CT multi-organ segmentation. We have validated the performance of our method on the Multi Atlas Labeling Beyond The Cranial Vault (BTCV) dataset for abdomen mult-organ segmentation and the SinoMed Whole Body (SMWB) dataset for body multi-organ segmentation tasks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。