在医学图像分割中结合AI与公民科学以创建增强训练数据集

Diversity-Promoting Ensemble for Medical Image Segmentation

Medical image segmentation is an actively studied task in medical imaging, where the precision of the annotations is of utter importance towards accurate diagnosis and treatment. In recent years, the task has been approached with various deep learning systems, among the most popular models being U-Net. In this work, we propose a novel strategy to generate ensembles of different architectures for medical image segmentation, by leveraging the diversity (decorrelation) of the models forming the ensemble. More specifically, we utilize the Dice score among model pairs to estimate the correlation be…

用于增强皮肤病变分割的 Transformer-CNN 融合架构

Transformer-CNN Fused Architecture for Enhanced Skin Lesion Segmentation

本文针对 CNN 在医学图像分割中难以捕获长程依赖和全局上下文的问题，提出一种融合 Transformer 与 CNN 的混合架构，并通过多组实验验证其在皮肤病变分割中的有效性。

用于医学图像分割任务的潜在空间扩散模型

Diffusion Model in Latent Space for Medical Image Segmentation Task

MedSegLatDiff结合VAE和潜在扩散模型，通过加权交叉熵损失提升医学图像分割的精度与可靠性，适用于临床部署。

评估内存高效医学图像生成的效用：一项关于肺结节分割的研究

Evaluating Utility of Memory Efficient Medical Image Generation: A Study on Lung Nodule Segmentation

提出一种内存高效的逐块DDPM生成带肺结节分割的合成CT图像，实验表明仅用合成数据训练或用于增强真实数据均能提升分割性能，有效缓解医学影像数据稀缺问题。

Structured dataset documentation: a datasheet for CheXpert

Billions of X-ray images are taken worldwide each year. Machine learning, and deep learning in particular, has shown potential to help radiologists triage and diagnose images. However, deep learning requires large datasets with reliable labels. The CheXpert dataset was created with the participation of board-certified radiologists, resulting in the strong ground truth needed to train deep learning networks. Following the structured format of Datasheets for Datasets, this paper expands on the original CheXpert paper and other sources to show the critical role played by radiologists in the creat…

Deep Learning in Medical Image Registration: A Survey

The establishment of image correspondence through robust image registration is critical to many clinical tasks such as image fusion, organ atlas creation, and tumor growth monitoring, and is a very challenging problem. Since the beginning of the recent deep learning renaissance, the medical imaging research community has developed deep learning based approaches and achieved the state-of-the-art in many applications, including image registration. The rapid adoption of deep learning for image registration applications over the past few years necessitates a comprehensive summary and outlook, whic…