Predicting unseen antibodies’ neutralizability via adaptive graph neural networks

The effects of novel antibodies are hard to predict owing to the complex interactions between antibodies and antigens. Zhang and colleagues use a graph-based method to learn a dynamic representation that allows for predictions of neutralization activity and demonstrate the method by recommending probable antibodies for human immunodeficiency virus, severe acute respiratory syndrome coronavirus 2, influenza and dengue. Most natural and synthetic antibodies are 'unseen'. That is, the demonstration of their neutralization effects with any antigen requires laborious and costly wet-lab experiments. The existing methods that learn antibody representations from known antibody-antigen interactions are unsuitable for unseen antibodies owing to the absence of interaction instances. The DeepAAI method proposed herein learns unseen antibody representations by constructing two adaptive relation graphs among antibodies and antigens and applying Laplacian smoothing between unseen and seen antibodies' representations. Rather than using static protein descriptors, DeepAAI learns representations and relation graphs 'dynamically', optimized towards the downstream tasks of neutralization prediction and 50% inhibition concentration estimation. The performance of DeepAAI is demonstrated on human immunodeficiency virus, severe acute respiratory syndrome coronavirus 2, influenza and dengue. Moreover, the relation graphs have rich interpretability. The antibody relation graph implies similarity in antibody neutralization reactions, and the antigen relation graph indicates the relation among a virus's different variants. We accordingly recommend probable broad-spectrum antibodies against new variants of these viruses.

Learning COVID-19 Pneumonia Lesion Segmentation From Imperfect Annotations via Divergence-Aware Selective Training.

Automatic segmentation of COVID-19 pneumonia lesions is critical for quantitative measurement for diagnosis and treatment management. For this task, deep learning is the state-of-the-art method while requires a large set of accurately annotated images for training, which is difficult to obtain due to limited access to experts and the time-consuming annotation process. To address this problem, we aim to train the segmentation network from imperfect annotations, where the training set consists of a small clean set of accurately annotated images by experts and a large noisy set of inaccurate annotations by non-experts. To avoid the labels with different qualities corrupting the segmentation model, we propose a new approach to train segmentation networks to deal with noisy labels. We introduce a dual-branch network to separately learn from the accurate and noisy annotations. To fully exploit the imperfect annotations as well as suppressing the noise, we design a Divergence-Aware Selective Training (DAST) strategy, where a divergence-aware noisiness score is used to identify severely noisy annotations and slightly noisy annotations. For severely noisy samples we use an regularization through dual-branch consistency between predictions from the two branches. We also refine slightly noisy samples and use them as supplementary data for the clean branch to avoid overfitting. Experimental results show that our method achieves a higher performance than standard training process for COVID-19 pneumonia lesion segmentation when learning from imperfect labels, and our framework outperforms the state-of-the-art noise-tolerate methods significantly with various clean label percentages.

A Denoising Self-supervised Approach for COVID-19 Pneumonia Lesion Segmentation with Limited Annotated CT Images.

The coronavirus disease 2019 (COVID-19) has become a global pandemic. The segmentation of COVID-19 pneumonia lesions from CT images is important in quantitative evaluation and assessment of the infection. Though many deep learning segmentation methods have been proposed, the performance is limited when pixel-level annotations are hard to obtain. In order to alleviate the performance limitation brought by the lack of pixel-level annotation in COVID-19 pneumonia lesion segmentation task, we construct a denoising self-supervised framework, which is composed of a pretext denoising task and a downstream segmentation task. Through the pretext denoising task, the semantic features from massive unlabelled data are learned in an unsupervised manner, so as to provide additional supervisory signal for the downstream segmentation task. Experimental results showed that our method can effectively leverage unlabelled images to improve the segmentation performance, and outperformed reconstruction-based self-supervised learning when only a small set of training images are annotated.Clinical relevance-The proposed method can effectively leverage unlabelled images to improve the performance for COVID-19 pneumonia lesion segmentation when only a small set of CT images are annotated.

A Noise-Robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions From CT Images.

Segmentation of pneumonia lesions from CT scans of COVID-19 patients is important for accurate diagnosis and follow-up. Deep learning has a potential to automate this task but requires a large set of high-quality annotations that are difficult to collect. Learning from noisy training labels that are easier to obtain has a potential to alleviate this problem. To this end, we propose a novel noise-robust framework to learn from noisy labels for the segmentation task. We first introduce a noise-robust Dice loss that is a generalization of Dice loss for segmentation and Mean Absolute Error (MAE) loss for robustness against noise, then propose a novel COVID-19 Pneumonia Lesion segmentation network (COPLE-Net) to better deal with the lesions with various scales and appearances. The noise-robust Dice loss and COPLE-Net are combined with an adaptive self-ensembling framework for training, where an Exponential Moving Average (EMA) of a student model is used as a teacher model that is adaptively updated by suppressing the contribution of the student to EMA when the student has a large training loss. The student model is also adaptive by learning from the teacher only when the teacher outperforms the student. Experimental results showed that: (1) our noise-robust Dice loss outperforms existing noise-robust loss functions, (2) the proposed COPLE-Net achieves higher performance than state-of-the-art image segmentation networks, and (3) our framework with adaptive self-ensembling significantly outperforms a standard training process and surpasses other noise-robust training approaches in the scenario of learning from noisy labels for COVID-19 pneumonia lesion segmentation.