The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

In this paper we present Mask DINO, a unified object detection and segmentation framework. Mask DINO extends DINO (DETR with Improved Denoising Anchor Boxes) by adding a mask prediction branch which supports all image segmentation tasks (instance, panoptic, and semantic). It makes use of the query embeddings from DINO to dot-product a high-resolution pixel embedding map to predict a set of binary masks. Some key components in DINO are extended for segmentation through a shared architecture and training process. Mask DINO is simple, efficient, and scalable, and it can benefit from joint large-scale detection and segmentation datasets. Our experiments show that Mask DINO significantly outperforms all existing specialized segmentation methods, both on a ResNet-50 backbone and a pre-trained model with SwinL backbone. Notably, Mask DINO establishes the best results to date on instance segmentation (54.5 AP on COCO), panoptic segmentation (59.4 PQ on COCO), and semantic segmentation (60.8 mIoU on ADE20K) among models under one billion parameters. Code is available at \url{https://github.com/IDEACVR/MaskDINO}.

我们将Dino（\ textbf {d} etr与\ textbf {i} mpred de \ textbf {n} oising hand \ textbf {o} r boxes），一种最先进的端到端对象检测器。 ％ 在本文中。 Dino通过使用一种对比度方法来降级训练，一种用于锚定初始化的混合查询选择方法以及对盒子预测的两次方案，通过使用对比的方式来改善性能和效率的模型。 Dino在$ 12 $时代获得$ 49.4 $ ap，$ 12.3 $ ap in Coco $ 24 $时期，带有Resnet-50骨干和多尺度功能，可显着改善$ \ textbf {+6.0} $ \ textbf {ap}和ap {ap}和ap}和$ \ textbf {+2.7} $ \ textbf {ap}与以前的最佳detr样模型相比，分别是dn-detr。 Dino在模型大小和数据大小方面都很好地缩放。没有铃铛和哨子，在对objects365数据集进行了swinl骨架的预训练后，Dino在两个Coco \ texttt {val2017}（$ \ textbf {63.2} $ \ textbf {ap ap}）和\ testtt { -dev}（\ textbf {$ \ textbf {63.3} $ ap}）。与排行榜上的其他模型相比，Dino大大降低了其模型大小和预训练数据大小，同时实现了更好的结果。我们的代码将在\ url {https://github.com/ideacvr/dino}提供。

We present in this paper a novel denoising training method to speedup DETR (DEtection TRansformer) training and offer a deepened understanding of the slow convergence issue of DETR-like methods. We show that the slow convergence results from the instability of bipartite graph matching which causes inconsistent optimization goals in early training stages. To address this issue, except for the Hungarian loss, our method additionally feeds ground-truth bounding boxes with noises into Transformer decoder and trains the model to reconstruct the original boxes, which effectively reduces the bipartite graph matching difficulty and leads to a faster convergence. Our method is universal and can be easily plugged into any DETR-like methods by adding dozens of lines of code to achieve a remarkable improvement. As a result, our DN-DETR results in a remarkable improvement ($+1.9$AP) under the same setting and achieves the best result (AP $43.4$ and $48.6$ with $12$ and $50$ epochs of training respectively) among DETR-like methods with ResNet-$50$ backbone. Compared with the baseline under the same setting, DN-DETR achieves comparable performance with $50\%$ training epochs. Code is available at \url{https://github.com/FengLi-ust/DN-DETR}.

心肌活力的评估对于患有心肌梗塞的患者的诊断和治疗管理是必不可少的，并且心肌病理学的分类是本评估的关键。这项工作定义了医学图像分析的新任务，即进行心肌病理分割（MYOPS）结合三个序列的心脏磁共振（CMR）图像，该图像首次与Mycai 2020一起在Myops挑战中提出的。挑战提供了45个配对和预对准的CMR图像，允许算法将互补信息与三个CMR序列组合到病理分割。在本文中，我们提供了挑战的详细信息，从十五个参与者的作品调查，并根据五个方面解释他们的方法，即预处理，数据增强，学习策略，模型架构和后处理。此外，我们对不同因素的结果分析了结果，以检查关键障碍和探索解决方案的潜力，以及为未来的研究提供基准。我们得出结论，虽然报告了有前途的结果，但研究仍处于早期阶段，在成功应用于诊所之前需要更深入的探索。请注意，MyOPS数据和评估工具继续通过其主页（www.sdspeople.fudan.edu.cn/zhuangxiahai/0/myops20 /）注册注册。

一阶逻辑的传统自动定理普通普及依赖于速度优化的搜索和许多手工制的启发式，旨在在各种域中工作。文献中的机器学习方法取决于这些传统的传统普通普通的自我或达到了可比性的比较短暂。在本文中，我们提出了一般的增量学习算法，用于培训域特定竞争员的一阶逻辑，而不是基本的给定 - 子算法，但使用学习的子句评分函数。子句被用作图表，并呈现给具有光谱特征的变压器网络。为了解决稀疏性和初步缺乏培训数据以及缺乏自然课程，我们适应后敏感经验重播到定理证明，即使没有找到证据。我们展示了这种方式培训的普通培训可以在TPTP数据集中匹配和有时会在证明的数量和质量方面超越TPTP数据集。

Manual prescription of the field of view (FOV) by MRI technologists is variable and prolongs the scanning process. Often, the FOV is too large or crops critical anatomy. We propose a deep-learning framework, trained by radiologists' supervision, for automating FOV prescription. An intra-stack shared feature extraction network and an attention network are used to process a stack of 2D image inputs to generate output scalars defining the location of a rectangular region of interest (ROI). The attention mechanism is used to make the model focus on the small number of informative slices in a stack. Then the smallest FOV that makes the neural network predicted ROI free of aliasing is calculated by an algebraic operation derived from MR sampling theory. We retrospectively collected 595 cases between February 2018 and February 2022. The framework's performance is examined quantitatively with intersection over union (IoU) and pixel error on position, and qualitatively with a reader study. We use the t-test for comparing quantitative results from all models and a radiologist. The proposed model achieves an average IoU of 0.867 and average ROI position error of 9.06 out of 512 pixels on 80 test cases, significantly better (P<0.05) than two baseline models and not significantly different from a radiologist (P>0.12). Finally, the FOV given by the proposed framework achieves an acceptance rate of 92% from an experienced radiologist.

最近，致力于通过现代机器学习方法预测脑部疾病的最新神经影像学研究通常包括单一模态并依靠监督的过度参数化模型。但是，单一模态仅提供了高度复杂的大脑的有限视图。至关重要的是，临床环境中的有监督模型缺乏用于培训的准确诊断标签。粗标签不会捕获脑疾病表型的长尾谱，这导致模型的普遍性丧失，从而使它们在诊断环境中的有用程度降低。这项工作提出了一个新型的多尺度协调框架，用于从多模式神经影像数据中学习多个表示。我们提出了一般的归纳偏见分类法，以捕获多模式自学融合中的独特和联合信息。分类法构成了一个无解码器模型的家族，具有降低的计算复杂性，并捕获多模式输入的本地和全局表示之间的多尺度关系。我们使用各种阿尔茨海默氏病表型中使用功能和结构磁共振成像（MRI）数据对分类法进行了全面评估，并表明自我监督模型揭示了与疾病相关的大脑区域和多模态链接，而无需在预先访问PRE-PRE-the PRE-the PRE-the PRE-the PRE-PRECTEN NICKES NOCKER NOCKER NOCKER NOCKER NOCKER NOCE访问。训练。拟议的多模式自学学习的学习能够表现出两种模式的分类表现。伴随的丰富而灵活的无监督的深度学习框架捕获了复杂的多模式关系，并提供了符合或超过更狭窄的监督分类分析的预测性能。我们提供了详尽的定量证据，表明该框架如何显着提高我们对复杂脑部疾病中缺失的联系的搜索。

近似消息传递（AMP）类型算法已被广泛用于某些大型随机线性系统的信号重建。AMP型算法的关键特征是可以通过状态进化正确描述其动力学。但是，状态进化不一定保证迭代算法的收敛性。为了解决原则上AMP类型算法的收敛问题，本文提出了在足够的统计条件下的记忆AMP（MAMP），称为足够的统计MAMP（SS-MAMP）。我们表明，SS-MAMP的协方差矩阵是L带和收敛的。给定任意启动，我们可以通过阻尼来构建SS-MAMP，这不仅可以确保收敛性，而且可以保留正交性，即可以通过状态进化正确描述其动力学。

近似消息传递（AMP）是一种希望具有非高斯信令的某些高维线性系统的未知信号重建的有希望的技术。 AMP型算法的杰出特征是它们的动态可以通过状态演进来严格描述。但是，状态的进化不一定保证迭代算法的融合。为了解决AMP型算法的收敛问题原则上，本文提出了一种在足够的统计条件下的存储放大器（MAMP），命名为足够的统计MAMP（SS-MAMP）。我们表明SS-MAMP的协方差矩阵是L-带状和会聚。考虑到任意的MAMP，我们可以通过阻尼构造SS-MAMP，这不仅可以确保MAMP的收敛，而且还可以保留MAMP的正交性，即，其动态可以通过状态演变严格地描述。作为副产品，我们证明贝叶斯最佳正交/载体放大器（Bo-Oamp / Vamp）是SS-MAMP。结果，我们揭示了大型系统的Bo-Oamp /鞋面的两个有趣特性：1）协方差矩阵是L型带状的，并且在BO-Oamp / vamp中收敛，2）阻尼和存储器无用（即，做在BO-OAMP / VAMP中没有带来性能改进。作为一个例子，我们构建了一个足够的统计贝叶斯 - 最佳MAMP（BO-MAMP），如果其状态进化具有独特的固定点，并且其MSE比原来的BO-MAMP更糟糕，那么它是最佳的。最后，提供了模拟以验证理论结果的有效性和准确性。