Deep learning has been widely used for protein engineering. However, it is limited by the lack of sufficient experimental data to train an accurate model for predicting the functional fitness of high-order mutants. Here, we develop SESNet, a supervised deep-learning model to predict the fitness for protein mutants by leveraging both sequence and structure information, and exploiting attention mechanism. Our model integrates local evolutionary context from homologous sequences, the global evolutionary context encoding rich semantic from the universal protein sequence space and the structure information accounting for the microenvironment around each residue in a protein. We show that SESNet outperforms state-of-the-art models for predicting the sequence-function relationship on 26 deep mutational scanning datasets. More importantly, we propose a data augmentation strategy by leveraging the data from unsupervised models to pre-train our model. After that, our model can achieve strikingly high accuracy in prediction of the fitness of protein mutants, especially for the higher order variants (> 4 mutation sites), when finetuned by using only a small number of experimental mutation data (<50). The strategy proposed is of great practical value as the required experimental effort, i.e., producing a few tens of experimental mutation data on a given protein, is generally affordable by an ordinary biochemical group and can be applied on almost any protein.

Background and objective: COVID-19 and its variants have caused significant disruptions in over 200 countries and regions worldwide, affecting the health and lives of billions of people. Detecting COVID-19 from chest X-Ray (CXR) images has become one of the fastest and easiest methods for detecting COVID-19 since the common occurrence of radiological pneumonia findings in COVID-19 patients. We present a novel high-accuracy COVID-19 detection method that uses CXR images. Methods: Our method consists of two phases. One is self-supervised learning-based pertaining; the other is batch knowledge ensembling-based fine-tuning. Self-supervised learning-based pretraining can learn distinguished representations from CXR images without manually annotated labels. On the other hand, batch knowledge ensembling-based fine-tuning can utilize category knowledge of images in a batch according to their visual feature similarities to improve detection performance. Unlike our previous implementation, we introduce batch knowledge ensembling into the fine-tuning phase, reducing the memory used in self-supervised learning and improving COVID-19 detection accuracy. Results: On two public COVID-19 CXR datasets, namely, a large dataset and an unbalanced dataset, our method exhibited promising COVID-19 detection performance. Our method maintains high detection accuracy even when annotated CXR training images are reduced significantly (e.g., using only 10% of the original dataset). In addition, our method is insensitive to changes in hyperparameters. Conclusions: The proposed method outperforms other state-of-the-art COVID-19 detection methods in different settings. Our method can reduce the workloads of healthcare providers and radiologists.

Purpose: Considering several patients screened due to COVID-19 pandemic, computer-aided detection has strong potential in assisting clinical workflow efficiency and reducing the incidence of infections among radiologists and healthcare providers. Since many confirmed COVID-19 cases present radiological findings of pneumonia, radiologic examinations can be useful for fast detection. Therefore, chest radiography can be used to fast screen COVID-19 during the patient triage, thereby determining the priority of patient's care to help saturated medical facilities in a pandemic situation. Methods: In this paper, we propose a new learning scheme called self-supervised transfer learning for detecting COVID-19 from chest X-ray (CXR) images. We compared six self-supervised learning (SSL) methods (Cross, BYOL, SimSiam, SimCLR, PIRL-jigsaw, and PIRL-rotation) with the proposed method. Additionally, we compared six pretrained DCNNs (ResNet18, ResNet50, ResNet101, CheXNet, DenseNet201, and InceptionV3) with the proposed method. We provide quantitative evaluation on the largest open COVID-19 CXR dataset and qualitative results for visual inspection. Results: Our method achieved a harmonic mean (HM) score of 0.985, AUC of 0.999, and four-class accuracy of 0.953. We also used the visualization technique Grad-CAM++ to generate visual explanations of different classes of CXR images with the proposed method to increase the interpretability. Conclusions: Our method shows that the knowledge learned from natural images using transfer learning is beneficial for SSL of the CXR images and boosts the performance of representation learning for COVID-19 detection. Our method promises to reduce the incidence of infections among radiologists and healthcare providers.

In this paper, we propose an end-to-end framework that jointly learns keypoint detection, descriptor representation and cross-frame matching for the task of image-based 3D localization. Prior art has tackled each of these components individually, purportedly aiming to alleviate difficulties in effectively train a holistic network. We design a self-supervised image warping correspondence loss for both feature detection and matching, a weakly-supervised epipolar constraints loss on relative camera pose learning, and a directional matching scheme that detects key-point features in a source image and performs coarse-to-fine correspondence search on the target image. We leverage this framework to enforce cycle consistency in our matching module. In addition, we propose a new loss to robustly handle both definite inlier/outlier matches and less-certain matches. The integration of these learning mechanisms enables end-to-end training of a single network performing all three localization components. Bench-marking our approach on public data-sets, exemplifies how such an end-to-end framework is able to yield more accurate localization that out-performs both traditional methods as well as state-of-the-art weakly supervised methods.

Video event extraction aims to detect salient events from a video and identify the arguments for each event as well as their semantic roles. Existing methods focus on capturing the overall visual scene of each frame, ignoring fine-grained argument-level information. Inspired by the definition of events as changes of states, we propose a novel framework to detect video events by tracking the changes in the visual states of all involved arguments, which are expected to provide the most informative evidence for the extraction of video events. In order to capture the visual state changes of arguments, we decompose them into changes in pixels within objects, displacements of objects, and interactions among multiple arguments. We further propose Object State Embedding, Object Motion-aware Embedding and Argument Interaction Embedding to encode and track these changes respectively. Experiments on various video event extraction tasks demonstrate significant improvements compared to state-of-the-art models. In particular, on verb classification, we achieve 3.49% absolute gains (19.53% relative gains) in F1@5 on Video Situation Recognition.

数据集复杂性评估旨在在训练分类器之前先预测具有复杂性计算的数据集上的分类性能，该分类器也可以用于分类器选择和减少数据集。深卷积神经网络（DCNN）的训练过程是迭代的且耗时的，这是由于高参数的不确定性和不同数据集引入的域移位。因此，通过在培训DCNN模型之前有效评估数据集的复杂性来预测分类性能是有意义的。本文提出了一种新的方法，称为Laplacian Spectrum（CMSAUL）下的累积最大缩放区域，该方法可以在六个数据集上实现最新的复杂性评估性能。

背景和目标：需要分享医疗数据以实现医疗保健信息的跨机构流量并构建高准确的计算机辅助诊断系统。但是，大量的医疗数据集，保存深度卷积神经网络（DCNN）模型的大量记忆以及患者的隐私保护是可能导致医疗数据共享效率低下的问题。因此，本研究提出了一种新型的软标签数据集蒸馏方法，用于医疗数据共享。方法：所提出的方法提炼医疗图像数据的有效信息，并生成几个带有不同数据分布的压缩图像，以供匿名医疗数据共享。此外，我们的方法可以提取DCNN模型的基本权重，以减少保存训练有素的模型以进行有效的医疗数据共享所需的内存。结果：所提出的方法可以将数万张图像压缩为几个软标签图像，并将受过训练的模型的大小减少到其原始大小的几百分之一。蒸馏后获得的压缩图像已在视觉上匿名化；因此，它们不包含患者的私人信息。此外，我们可以通过少量压缩图像实现高检测性能。结论：实验结果表明，所提出的方法可以提高医疗数据共享的效率和安全性。

高级模型的采集取决于许多领域的大型数据集，这使存储数据集和培训模型昂贵。作为解决方案，数据集蒸馏可以合成一个小数据集，以便在其上训练有素的模型在与原始大型数据集的情况下达到高性能。通过匹配网络参数的最近提出的数据集蒸馏方法已被证明对多个数据集有效。但是，蒸馏过程中的一些参数很难匹配，这会损害蒸馏性能。基于此观察结果，本文提出了一种使用参数修剪来解决问题的新方法。提出的方法可以通过在蒸馏过程中修剪难以匹配的参数来合成更强大的蒸馏数据集并改善蒸馏性能。三个数据集的实验结果表明，所提出的方法的表现优于其他SOTA数据集蒸馏方法。

由于存在隐私保护问题以及传输和存储许多高分辨率医疗图像的巨大成本，因此在医院之间共享医疗数据集很具有挑战性。但是，数据集蒸馏可以合成一个小数据集，从而使对其进行训练的模型与原始大型数据集实现了可比的性能，这显示了解决现有的医疗共享问题的潜力。因此，本文提出了一种基于数据集蒸馏的新型医学数据集共享方法。Covid-19胸部X射线图像数据集的实验结果表明，即使使用稀缺的匿名胸部X射线图像，我们的方法也可以达到高检测性能。

随着移动平台上对计算摄影和成像的需求不断增长，在相机系统中开发和集成了高级图像传感器与新型算法的发展。但是，缺乏用于研究的高质量数据以及从行业和学术界进行深入交流的难得的机会限制了移动智能摄影和成像（MIPI）的发展。为了弥合差距，我们介绍了第一个MIPI挑战，包括五个曲目，这些曲目着重于新型图像传感器和成像算法。在本文中，引入了RGBW关节Remosaic和Denoise，这是五个曲目之一，在全面分辨率上进行了RGBW CFA插值的插值。为参与者提供了一个新的数据集，其中包括70（培训）和15个（验证）高质量RGBW和拜耳对的场景。此外，对于每个场景，在0dB，24dB和42dB上提供了不同噪声水平的RGBW。所有数据均在室外和室内条件下使用RGBW传感器捕获。最终结果是使用PSNR，SSIM，LPIPS和KLD在内的客观指标评估的。本文提供了此挑战中所有模型的详细描述。有关此挑战的更多详细信息以及数据集的链接，请访问https://github.com/mipi-challenge/mipi2022。