$ t_ {1 \ rho} $映射是一种有希望的定量MRI技术，用于对组织性质的非侵入性评估。基于学习的方法可以从减少数量的$ t_ {1 \ rho} $加权图像中映射$ t_ {1 \ rho} $，但需要大量的高质量培训数据。此外，现有方法不提供$ t_ {1 \ rho} $估计的置信度。为了解决这些问题，我们提出了一个自我监督的学习神经网络，该网络使用学习过程中的放松约束来学习$ t_ {1 \ rho} $映射。为$ t_ {1 \ rho} $量化网络建立了认知不确定性和态度不确定性，以提供$ t_ {1 \ rho} $映射的贝叶斯置信度估计。不确定性估计还可以使模型规范化，以防止其学习不完美的数据。我们对52例非酒精性脂肪肝病患者收集的$ T_ {1 \ rho} $数据进行了实验。结果表明，我们的方法优于$ t_ {1 \ rho} $量化肝脏的现有方法，使用少于两个$ t_ {1 \ rho} $加权图像。我们的不确定性估计提供了一种可行的方法，可以建模基于自我监督学习的$ t_ {1 \ rho} $估计的信心，这与肝脏中的现实$ t_ {1 \ rho} $成像是一致的。

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.

Image super-resolution is a common task on mobile and IoT devices, where one often needs to upscale and enhance low-resolution images and video frames. While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints. In this Mobile AI challenge, we address this problem and propose the participants to design an efficient quantized image super-resolution solution that can demonstrate a real-time performance on mobile NPUs. The participants were provided with the DIV2K dataset and trained INT8 models to do a high-quality 3X image upscaling. The runtime of all models was evaluated on the Synaptics VS680 Smart Home board with a dedicated edge NPU capable of accelerating quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 60 FPS rate when reconstructing Full HD resolution images. A detailed description of all models developed in the challenge is provided in this paper.

气道分割对于胸部CT图像分析至关重要。但是，由于固有的复杂树状结构和气道分支的不平衡大小，这仍然是一项具有挑战性的任务。当前的深度学习方法着眼于模型结构设计，而培训策略和损失功能的潜力尚未得到充分探索。因此，我们提出了一个简单而有效的气道分割管道，该管道表示为Naviairway，它发现具有支气管敏感的损失功能和人类视觉启发的迭代训练策略，发现了更细的细支气管。实验结果表明，Naverway的表现优于现有方法，尤其是在识别高产生的细支气管和对新CT扫描的鲁棒性方面。此外，纳维亚威是一般的。它可以与不同的骨干模型结合使用，并显着提高其性能。此外，我们建议对基于深度学习的气道细分方法进行更全面，更公平的评估，以更全面，更公平地评估。 Naveraway可以生成用于导航支气管镜检查的气道路线图，并且在生物医学图像中细分精细和长管结构时，也可以应用于其他情况。该代码可在https://github.com/antonotnawang/naviairway上公开获得。

Objective: Thigh muscle group segmentation is important for assessment of muscle anatomy, metabolic disease and aging. Many efforts have been put into quantifying muscle tissues with magnetic resonance (MR) imaging including manual annotation of individual muscles. However, leveraging publicly available annotations in MR images to achieve muscle group segmentation on single slice computed tomography (CT) thigh images is challenging. Method: We propose an unsupervised domain adaptation pipeline with self-training to transfer labels from 3D MR to single CT slice. First, we transform the image appearance from MR to CT with CycleGAN and feed the synthesized CT images to a segmenter simultaneously. Single CT slices are divided into hard and easy cohorts based on the entropy of pseudo labels inferenced by the segmenter. After refining easy cohort pseudo labels based on anatomical assumption, self-training with easy and hard splits is applied to fine tune the segmenter. Results: On 152 withheld single CT thigh images, the proposed pipeline achieved a mean Dice of 0.888(0.041) across all muscle groups including sartorius, hamstrings, quadriceps femoris and gracilis. muscles Conclusion: To our best knowledge, this is the first pipeline to achieve thigh imaging domain adaptation from MR to CT. The proposed pipeline is effective and robust in extracting muscle groups on 2D single slice CT thigh images.The container is available for public use at https://github.com/MASILab/DA_CT_muscle_seg

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

Various depth estimation models are now widely used on many mobile and IoT devices for image segmentation, bokeh effect rendering, object tracking and many other mobile tasks. Thus, it is very crucial to have efficient and accurate depth estimation models that can run fast on low-power mobile chipsets. In this Mobile AI challenge, the target was to develop deep learning-based single image depth estimation solutions that can show a real-time performance on IoT platforms and smartphones. For this, the participants used a large-scale RGB-to-depth dataset that was collected with the ZED stereo camera capable to generated depth maps for objects located at up to 50 meters. The runtime of all models was evaluated on the Raspberry Pi 4 platform, where the developed solutions were able to generate VGA resolution depth maps at up to 27 FPS while achieving high fidelity results. All models developed in the challenge are also compatible with any Android or Linux-based mobile devices, their detailed description is provided in this paper.

2D低剂量单板腹部计算机断层扫描（CT）切片可直接测量身体成分，这对于对衰老的健康关系进行定量表征至关重要。然而，由于不同年内获得的纵向切片之间的位置方差，使用2D腹部切片对人体成分变化的纵向分析具有挑战性。为了减少位置差异，我们将条件生成模型扩展到我们的C-斜肌，该模型在腹部区域进行任意轴向切片作为条件，并通过估计潜在空间的结构变化来生成定义的椎骨水平切片。对来自内部数据集的1170名受试者的实验和BTCV Miccai挑战赛的50名受试者的实验表明，我们的模型可以从现实主义和相似性方面产生高质量的图像。来自巴尔的摩纵向研究（BLSA）数据集的20名受试者的外部实验，其中包含纵向单腹部切片验证了我们的方法可以在肌肉和内脏脂肪面积方面与切片的位置方差进行协调。我们的方法提供了一个有希望的方向，将切片从不同的椎骨水平映射到目标切片，以减少单个切片纵向分析的位置差异。源代码可在以下网址获得：https：//github.com/masilab/c-slicegen。

Transformer-based models, capable of learning better global dependencies, have recently demonstrated exceptional representation learning capabilities in computer vision and medical image analysis. Transformer reformats the image into separate patches and realize global communication via the self-attention mechanism. However, positional information between patches is hard to preserve in such 1D sequences, and loss of it can lead to sub-optimal performance when dealing with large amounts of heterogeneous tissues of various sizes in 3D medical image segmentation. Additionally, current methods are not robust and efficient for heavy-duty medical segmentation tasks such as predicting a large number of tissue classes or modeling globally inter-connected tissues structures. Inspired by the nested hierarchical structures in vision transformer, we proposed a novel 3D medical image segmentation method (UNesT), employing a simplified and faster-converging transformer encoder design that achieves local communication among spatially adjacent patch sequences by aggregating them hierarchically. We extensively validate our method on multiple challenging datasets, consisting anatomies of 133 structures in brain, 14 organs in abdomen, 4 hierarchical components in kidney, and inter-connected kidney tumors). We show that UNesT consistently achieves state-of-the-art performance and evaluate its generalizability and data efficiency. Particularly, the model achieves whole brain segmentation task complete ROI with 133 tissue classes in single network, outperforms prior state-of-the-art method SLANT27 ensembled with 27 network tiles, our model performance increases the mean DSC score of the publicly available Colin and CANDI dataset from 0.7264 to 0.7444 and from 0.6968 to 0.7025, respectively.

从心脏病学到神经病学的疾病中，代谢健康越来越多地成为危险因素，身体成分的效率评估对于定量表征这些关系至关重要。 2D低剂量单切层扫描术（CT）提供了高分辨率，定量组织图，尽管视野有限。尽管在量化图像上下文时已经提出了许多潜在的分析，但尚无对低剂量单切片CT纵向变异性进行自动分割的全面研究。我们使用受监督的基于深度学习的细分和无监督的聚类方法研究了1469个巴尔的摩纵向研究（BLSA）腹部数据集的1469名纵向研究（BLSA）腹部数据集的1816片。在前两次扫描中有两年差距的1469名受试者中有300名被选出，以评估纵向变异性，其中包括类内相关系数（ICC）和变异系数（CV），以组织/器官的大小和平均强度为单位。我们表明，我们的分割方法在纵向环境中是稳定的，骰子范围为13个目标腹部组织结构的0.821至0.962。我们观察到ICC <0.5的大多数器官的较高变异性，肌肉，腹壁，脂肪和体膜的变化较低，平均ICC> 0.8。我们发现器官的变异性与2D切片的横截面位置高度相关。我们的努力铺平了定量探索和质量控制，以减少纵向分析中的不确定性。