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.

Self-supervised image denoising techniques emerged as convenient methods that allow training denoising models without requiring ground-truth noise-free data. Existing methods usually optimize loss metrics that are calculated from multiple noisy realizations of similar images, e.g., from neighboring tomographic slices. However, those approaches fail to utilize the multiple contrasts that are routinely acquired in medical imaging modalities like MRI or dual-energy CT. In this work, we propose the new self-supervised training scheme Noise2Contrast that combines information from multiple measured image contrasts to train a denoising model. We stack denoising with domain-transfer operators to utilize the independent noise realizations of different image contrasts to derive a self-supervised loss. The trained denoising operator achieves convincing quantitative and qualitative results, outperforming state-of-the-art self-supervised methods by 4.7-11.0%/4.8-7.3% (PSNR/SSIM) on brain MRI data and by 43.6-50.5%/57.1-77.1% (PSNR/SSIM) on dual-energy CT X-ray microscopy data with respect to the noisy baseline. Our experiments on different real measured data sets indicate that Noise2Contrast training generalizes to other multi-contrast imaging modalities.

Incorporating computed tomography (CT) reconstruction operators into differentiable pipelines has proven beneficial in many applications. Such approaches usually focus on the projection data and keep the acquisition geometry fixed. However, precise knowledge of the acquisition geometry is essential for high quality reconstruction results. In this paper, the differentiable formulation of fan-beam CT reconstruction is extended to the acquisition geometry. This allows to propagate gradient information from a loss function on the reconstructed image into the geometry parameters. As a proof-of-concept experiment, this idea is applied to rigid motion compensation. The cost function is parameterized by a trained neural network which regresses an image quality metric from the motion affected reconstruction alone. Using the proposed method, we are the first to optimize such an autofocus-inspired algorithm based on analytical gradients. The algorithm achieves a reduction in MSE by 35.5 % and an improvement in SSIM by 12.6 % over the motion affected reconstruction. Next to motion compensation, we see further use cases of our differentiable method for scanner calibration or hybrid techniques employing deep models.

低剂量计算机断层扫描（CT）降级算法旨在使常规CT采集中的患者剂量减少，同时保持高图像质量。最近，引入了深度学习〜（DL）的方法，由于其高模型容量，因此在此任务上的常规降级算法优于常规deno。但是，为了过渡基于DL的denoing到临床实践，这些数据驱动的方法必须超越可见的训练数据来概括地概括。因此，我们提出了一种由一组可训练的联合双边滤波器（JBF）组成的混合脱糖性方法，并结合了基于卷积DL的deNoising网络，以预测指导图像。我们提出的denoising管道结合了通过基于DL的功能提取和常规JBF的可靠性启用的高模型容量。通过在没有金属植入物的腹部CT扫描上进行训练以及对金属植入物以及头部CT数据进行腹部扫描测试，可以证明该管道的概括能力。当我们的管道中嵌入两个基于DL的DENOISER（RED-CNN/QAE）时，Denoisis的性能提高了$ 10 \，\％$/$ 82 \，\％$（RMSE）和$ 3 \，\％$ /$ 81 \，\％$（psnr）在包含金属的区域和$ 6 \，\％$/$ 78 \，\％$（rmse）和$ 2 \，\％$/$ 4 \，\％$（psnr）上与各自的香草模型相比，头部CT数据。最后，提出的可训练的JBFS限制了深神经网络的误差结合，以促进基于DL的DeOisers在低剂量CT管道中的适用性。

量子计算硬件的功能增加，并实现深量子电路的挑战需要完全自动化和有效的工具来编译量子电路。要以一系列与特定量子计算机体系结构有关的天然大门表达任意电路，对于使算法在量子硬件提供商的整个景观中可移植。在这项工作中，我们提出了一个能够转换和优化量子电路的编译器，针对基于穿梭的捕获离子量子处理器。它由剑桥量子计算机的量子电路框架pytket上的自定义算法组成。评估了广泛的量子电路的性能，与标准Pytket相比，与标准Qiskit汇编相比，栅极计数可以降低到3.6倍，最高为2.2，而我们获得的栅极计数与相似的栅极计数相比相比，针对AQT线性静态捕获离子地址架构的Pytket扩展。

无监督的域适应性（UDA）旨在将所学的知识从标记的源域转移到未标记的目标域。在UDA的背景下，对比度学习（CL）可以帮助更好地在特征空间中分开类。然而，在图像分割中，由于像素对比度损失的计算，较大的记忆足迹使其使用过度。此外，在医学成像中不容易获得标记的目标数据，并且获得新样品并不经济。结果，在这项工作中，当只有几个（几个）或单个（OneShot）图像可从目标域中获得时，我们将解决更具挑战性的UDA任务。我们应用样式转移模块来减轻目标样本的稀缺性。然后，为了使源和目标特征保持一致并解决传统对比损失的记忆问题，我们提出了基于质心的对比度学习（CCL）和质心规范规则器（CNR），以在方向和幅度上优化对比度对。此外，我们提出了多区域质心学习（MPCCL），以进一步降低目标特征的差异。对MS-CMRSEG数据集的几乎没有Shot评估表明，与基线相比，Cunduda在目标域上的分割性能提高了0.34的骰子分数，并且在更严格的Oneshot设置中提高了0.31骰子分数。

Machine learning models are typically evaluated by computing similarity with reference annotations and trained by maximizing similarity with such. Especially in the bio-medical domain, annotations are subjective and suffer from low inter- and intra-rater reliability. Since annotations only reflect the annotation entity's interpretation of the real world, this can lead to sub-optimal predictions even though the model achieves high similarity scores. Here, the theoretical concept of Peak Ground Truth (PGT) is introduced. PGT marks the point beyond which an increase in similarity with the reference annotation stops translating to better Real World Model Performance (RWMP). Additionally, a quantitative technique to approximate PGT by computing inter- and intra-rater reliability is proposed. Finally, three categories of PGT-aware strategies to evaluate and improve model performance are reviewed.

Many problems in machine learning involve bilevel optimization (BLO), including hyperparameter optimization, meta-learning, and dataset distillation. Bilevel problems consist of two nested sub-problems, called the outer and inner problems, respectively. In practice, often at least one of these sub-problems is overparameterized. In this case, there are many ways to choose among optima that achieve equivalent objective values. Inspired by recent studies of the implicit bias induced by optimization algorithms in single-level optimization, we investigate the implicit bias of gradient-based algorithms for bilevel optimization. We delineate two standard BLO methods -- cold-start and warm-start -- and show that the converged solution or long-run behavior depends to a large degree on these and other algorithmic choices, such as the hypergradient approximation. We also show that the inner solutions obtained by warm-start BLO can encode a surprising amount of information about the outer objective, even when the outer parameters are low-dimensional. We believe that implicit bias deserves as central a role in the study of bilevel optimization as it has attained in the study of single-level neural net optimization.

By optimizing the rate-distortion-realism trade-off, generative compression approaches produce detailed, realistic images, even at low bit rates, instead of the blurry reconstructions produced by rate-distortion optimized models. However, previous methods do not explicitly control how much detail is synthesized, which results in a common criticism of these methods: users might be worried that a misleading reconstruction far from the input image is generated. In this work, we alleviate these concerns by training a decoder that can bridge the two regimes and navigate the distortion-realism trade-off. From a single compressed representation, the receiver can decide to either reconstruct a low mean squared error reconstruction that is close to the input, a realistic reconstruction with high perceptual quality, or anything in between. With our method, we set a new state-of-the-art in distortion-realism, pushing the frontier of achievable distortion-realism pairs, i.e., our method achieves better distortions at high realism and better realism at low distortion than ever before.

In this paper, we introduce neural texture learning for 6D object pose estimation from synthetic data and a few unlabelled real images. Our major contribution is a novel learning scheme which removes the drawbacks of previous works, namely the strong dependency on co-modalities or additional refinement. These have been previously necessary to provide training signals for convergence. We formulate such a scheme as two sub-optimisation problems on texture learning and pose learning. We separately learn to predict realistic texture of objects from real image collections and learn pose estimation from pixel-perfect synthetic data. Combining these two capabilities allows then to synthesise photorealistic novel views to supervise the pose estimator with accurate geometry. To alleviate pose noise and segmentation imperfection present during the texture learning phase, we propose a surfel-based adversarial training loss together with texture regularisation from synthetic data. We demonstrate that the proposed approach significantly outperforms the recent state-of-the-art methods without ground-truth pose annotations and demonstrates substantial generalisation improvements towards unseen scenes. Remarkably, our scheme improves the adopted pose estimators substantially even when initialised with much inferior performance.