用于泛化能力评估的多中心息肉检测与分割数据集

Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge

Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, location, and surface largely affect identification, localisation, and characterisation. Moreover, colonoscopic surveillance and removal of polyps (referred to as polypectomy ) are highly operator-dependent procedures. There exist a high missed detection rate and incomplete removal of colonic polyps due to their variable nature, the difficulties to delineate the abnormality, the high recurrence rates, and the anatomical topography of the colon. There have been several developments in realisin…

RobustSurg: 解决领域泛化问题以实现分布外手术场景分割

RobustSurg: Tackling domain generalisation for out-of-distribution surgical scene segmentation

RobustSurg通过实例归一化和特征协方差映射技术提升手术场景分割的领域泛化能力，并引入恢复模块保留关键特征，同时提供新数据集以解决多类多中心数据不足问题。

Generalisability of fetal ultrasound deep learning models to low-resource imaging settings in five African countries

Most artificial intelligence (AI) research have concentrated in high-income countries, where imaging data, IT infrastructures and clinical expertise are plentiful. However, slower progress has been made in limited-resource environments where medical imaging is needed. For example, in Sub-Saharan Africa the rate of perinatal mortality is very high due to limited access to antenatal screening. In these countries, AI models could be implemented to help clinicians acquire fetal ultrasound planes for diagnosis of fetal abnormalities. So far, deep learning models have been proposed to identify stand…

一种全面的多模态数据集和基准用于内镜溃疡性结肠炎评分

A comprehensive multimodal dataset and benchmark for ulcerative colitis scoring in endoscopy

本文提出一个包含专家标注的MES和UCEIS评分及临床描述的多中心多分辨率数据集，结合双评分指标和专家生成的图像描述，为开发具有临床意义的多模态算法提供新机遇。

Generative Adversarial Networks for Video-to-Video Domain Adaptation

Endoscopic videos from multicentres often have different imaging conditions, e.g., color and illumination, which make the models trained on one domain usually fail to generalize well to another. Domain adaptation is one of the potential solutions to address the problem. However, few of existing works focused on the translation of video-based data. In this work, we propose a novel generative adversarial network (GAN), namely VideoGAN, to transfer the video-based data across different domains. As the frames of a video may have similar content and imaging conditions, the proposed VideoGAN has an …

可解释性引导的数据增强用于多中心结肠镜数据中的稳健分割

Interpretability-guided Data Augmentation for Robust Segmentation in Multi-centre Colonoscopy Data

针对多中心结肠镜图像分割泛化不足问题，提出基于可解释性显著性图的数据增强方法，增强模型稳健性，并在公开数据集上验证有效性。