Self-paced Multi-view Learning for CT-based severity assessment of COVID-19.

Prior studies for the task of severity assessment of COVID-19 (SA-COVID) usually suffer from domain-specific cognitive deficits. They mainly focus on visual cues based on single cognitive functions but fail to reconcile the valuable information from other alternative views. Inspired by the cognitive process of radiologists, this paper shifts naturally from single-symptom measurements to a multi-view analysis, and proposes a novel Self-paced Multi-view Learning (SPML) framework for automated SA-COVID. Specifically, the proposed SPML framework first comprehensively aggregates multi-view contexts in lung infection with different measure paradigms, i.e., Global Feature Branch, Texture Feature Branch, and Volume Feature Branch. In this way, multiple-perspective clues are taken into account to reflect the most essential pathological manifestation on CT images. To alleviate small-sample learning problems, we also introduce an optimization with self-paced learning strategy to cognitively increase the characterization capabilities of training samples by learning from simple to complex. In contrast to traditional batch-wise learning, a pure self-paced way can further guarantee the efficiency and accuracy of SPML when dealing with small and biased samples. Furthermore, we construct a well-established SA-COVID dataset that contains 300 CT images with fine annotations. Extensive experiments on this dataset demonstrate that SPML consistently outperforms the state-of-the-art baselines. The SA-COVID dataset is publicly released at https://github.com/YishuLiu/SA-COVID.

Study of SARS-CoV-2 transmission in urban environment by questionnaire and modeling for sustainable risk control.

Caused by SARS-CoV-2, COVID-19 has become a severe threaten to society and human health, its epidemic control emerges as long-term issue. A sustainable epidemic and environmental transmission risk control (SEERC) in urban area is urgently needed. This work aims to conduct a new investigation on the transmission risk of SARS-COV-2 as virus/hazardous material through various environmental medias, routes and regions in the entirely urban area for guiding the SEERC. Specifically, 5 routes in 28 regions (totally 140 scenarios) are considered. For a new perspective, the risk evaluation is conducted by the quantification of frontline medicals staffs' valuable experience in this work. 207 specialists responsible for the treatment of over 9000 infected patients are involved. The result showed that degree of risk was in the order of breath>contact-to-object>contact-to-human>intake>unknown. The modeling suggested source control as the prior measure for epidemic control. The combination of source control & mask wearing showed high efficiency in SEERC. The homeworking policy needed to cooperate with activity limitation to perform its efficiency. Subsequently, a new plan for SEERC was discussed. This work delivered significant information to researchers and decision makers for the further development of sustainable control for SARS-COV-2 spreading and COVID-19 epidemic.

A New Technique for Diagnosis of Dental Caries on the Children's First Permanent Molar

The current researches have been shown high prevalence and incidence of children's teeth caries, especially for the first permanent molar, which might do a lot of harm to their general health. Fortunately, early detection and protection can reduce the difficulty of treatment and protect children's oral health. However, traditional diagnostic methods such as dentist's visual inspection and radiographic imaging diagnosis are non-automatic and time-consuming. Given the COVID-19 epidemic, these methods should not be taken into consideration, since they fail to practice social distancing and further increase the risk of infection. To address these issues, in this paper we propose a novel caries detection and assessment (UCDA) framework to achieve a new technique for fully-automated diagnosis of dental caries on the children's first permanent molar. Inspired by an efficient in-network feature pyramid and anchor boxes, the proposed UCDA framework mainly contains a backbone network that is initialized with ResNet-FPN, and two parallel task-specific subnetworks for region regression and region classification. Due to the lack of the image database, we also present a novel children's oral image database, namely "Child-OID", which comprises 1, 368 primary school children's oral images with standard diagnostic annotations and labels, to evaluate the effectiveness of our UCDA method. Experiments on the Child-OID database demonstrate that commonly occurring caries on the first permanent molar can be more accurately detected via the proposed UCDA framework. Database and code are available at https://github.com/GipinLinn/UCDA-and-Child-OID.git.