有监督的深度学习算法具有自动化筛查，监视和分级的医学图像的巨大潜力。但是，培训表现模型通常需要大量的标记数据，这在医疗领域几乎无法获得。自我监督的对比框架通过首先从未标记的图像中学习来放松这种依赖性。在这项工作中，我们表明使用两种对比方法进行了预处理，即SIMCLR和BYOL，就与年龄相关的黄斑变性（AMD）的临床评估有关深度学习的实用性。在实验中，使用两个大型临床数据集，其中包含7,912名患者的170,427个光学相干断层扫描（OCT）图像，我们评估了从AMD阶段和类型分类到功能性终点的七个下游任务，从七个下游任务进行预处理，从在标签较少的七个任务中，六个任务中有六个显着增加。但是，标准的对比框架具有两个已知的弱点，这些弱点不利于医疗领域的预处理。用于创建正面对比对的几种图像转换不适用于灰度医学扫描。此外，医学图像通常描绘了相同的解剖区域和疾病的严重程度，从而导致许多误导性负面对。为了解决这些问题，我们开发了一种新颖的元数据增强方法，该方法利用了丰富的固有可用患者信息集。为此，我们采用了患者身份，眼睛位置（即左或右）和时间序列数据的记录，以指示典型的不可知的对比关系。通过利用这种经常被忽视的信息，我们元数据增强的对比预处理可带来进一步的好处，并且在下游七个任务中有五个任务中的五个中的五分之一。

最近的对比学习方法在低标签制度中实现了最新的。但是，培训需要大批量和重型增强，以创建图像的多个视图。使用非对抗性方法，负面因素被隐式地纳入损失中，允许不同的图像和模态作为对。尽管医学成像中的元信息（即年龄，性别）很丰富，但注释又嘈杂，容易出现阶级失衡。在这项工作中，我们使用纵向光学相干断层扫描（OCT）数据集利用了已经存在的时间信息（来自患者的不同访问），但使用时间知情的非对抗性损失（TINC），而没有增加复杂性和对负面对的需求。此外，我们的新颖配对方案可以避免重大增强，并将时间信息隐含地纳入对。最后，这些从训练中学到的表示在预测时间信息对于下游任务至关重要的情况下更为成功。更具体地说，我们的模型优于现有模型，可以预测从中期与年龄相关的黄斑变性（AMD）到晚期湿AMD阶段的转化风险。

With the rapid development of artificial intelligence (AI) in medical image processing, deep learning in color fundus photography (CFP) analysis is also evolving. Although there are some open-source, labeled datasets of CFPs in the ophthalmology community, large-scale datasets for screening only have labels of disease categories, and datasets with annotations of fundus structures are usually small in size. In addition, labeling standards are not uniform across datasets, and there is no clear information on the acquisition device. Here we release a multi-annotation, multi-quality, and multi-device color fundus image dataset for glaucoma analysis on an original challenge -- Retinal Fundus Glaucoma Challenge 2nd Edition (REFUGE2). The REFUGE2 dataset contains 2000 color fundus images with annotations of glaucoma classification, optic disc/cup segmentation, as well as fovea localization. Meanwhile, the REFUGE2 challenge sets three sub-tasks of automatic glaucoma diagnosis and fundus structure analysis and provides an online evaluation framework. Based on the characteristics of multi-device and multi-quality data, some methods with strong generalizations are provided in the challenge to make the predictions more robust. This shows that REFUGE2 brings attention to the characteristics of real-world multi-domain data, bridging the gap between scientific research and clinical application.

Robust forecasting of the future anatomical changes inflicted by an ongoing disease is an extremely challenging task that is out of grasp even for experienced healthcare professionals. Such a capability, however, is of great importance since it can improve patient management by providing information on the speed of disease progression already at the admission stage, or it can enrich the clinical trials with fast progressors and avoid the need for control arms by the means of digital twins. In this work, we develop a deep learning method that models the evolution of age-related disease by processing a single medical scan and providing a segmentation of the target anatomy at a requested future point in time. Our method represents a time-invariant physical process and solves a large-scale problem of modeling temporal pixel-level changes utilizing NeuralODEs. In addition, we demonstrate the approaches to incorporate the prior domain-specific constraints into our method and define temporal Dice loss for learning temporal objectives. To evaluate the applicability of our approach across different age-related diseases and imaging modalities, we developed and tested the proposed method on the datasets with 967 retinal OCT volumes of 100 patients with Geographic Atrophy, and 2823 brain MRI volumes of 633 patients with Alzheimer's Disease. For Geographic Atrophy, the proposed method outperformed the related baseline models in the atrophy growth prediction. For Alzheimer's Disease, the proposed method demonstrated remarkable performance in predicting the brain ventricle changes induced by the disease, achieving the state-of-the-art result on TADPOLE challenge.

视力障碍者的日常运动有重大问题。因此，我们以前的一些工作涉及计算机愿景来开发援助系统，以指导在关键情况下视力障碍。其中一些情况包括在室内和室外环境中的道路交叉路口和楼梯上的人行横道。本文为在此类关键情况下基于计算机视觉障碍的人提供了一个评估框架。提出的框架包括用于标记和存储指导方向的参考人类决策的接口，并将其与基于计算机视觉的决策进行比较。由于该研究领域中的严格评估方法并未明确定义，并且由于信息转移到视障人士的细节，因此提出了针对特定简化指导指令的评估标准。

光学相干断层扫描（OCT）是一种非侵入性的3D模态，广泛用于视网膜的眼科。在OCT上实现自动化的解剖学视网膜层分割对于检测和监测不同视网膜疾病（如年龄相关的黄斑病（AMD）或糖尿病性视网膜病）很重要。但是，大多数最先进的层分割方法基于纯监督的深度学习，需要大量的像素级注释数据，这些数据昂贵且难以获得。考虑到这一点，我们将半监督的范式介绍到视网膜层分割任务中，该任务利用大规模未标记数据集中存在的信息以及解剖学先验。特别是，一种新型的完全可区分的方法用于将表面位置回归转换为像素结构化分割，从而使以耦合方式同时使用1D表面和2D层表示来训练模型。特别是，这些2D分割被用作解剖因素，与学习的样式因子一起组成了用于重建输入图像的分离表示。同时，我们建议一组解剖学先验，以改善有限的标记数据时，可以改善网络训练。我们在使用中间和湿amd的现实世界中的扫描数据集上证明了我们的方法在使用我们的完整训练集时优于最先进带有标记数据的一部分。

Color fundus photography and Optical Coherence Tomography (OCT) are the two most cost-effective tools for glaucoma screening. Both two modalities of images have prominent biomarkers to indicate glaucoma suspected. Clinically, it is often recommended to take both of the screenings for a more accurate and reliable diagnosis. However, although numerous algorithms are proposed based on fundus images or OCT volumes in computer-aided diagnosis, there are still few methods leveraging both of the modalities for the glaucoma assessment. Inspired by the success of Retinal Fundus Glaucoma Challenge (REFUGE) we held previously, we set up the Glaucoma grAding from Multi-Modality imAges (GAMMA) Challenge to encourage the development of fundus \& OCT-based glaucoma grading. The primary task of the challenge is to grade glaucoma from both the 2D fundus images and 3D OCT scanning volumes. As part of GAMMA, we have publicly released a glaucoma annotated dataset with both 2D fundus color photography and 3D OCT volumes, which is the first multi-modality dataset for glaucoma grading. In addition, an evaluation framework is also established to evaluate the performance of the submitted methods. During the challenge, 1272 results were submitted, and finally, top-10 teams were selected to the final stage. We analysis their results and summarize their methods in the paper. Since all these teams submitted their source code in the challenge, a detailed ablation study is also conducted to verify the effectiveness of the particular modules proposed. We find many of the proposed techniques are practical for the clinical diagnosis of glaucoma. As the first in-depth study of fundus \& OCT multi-modality glaucoma grading, we believe the GAMMA Challenge will be an essential starting point for future research.