Lung cancer is one of the most deadly diseases around the world representing about 26% of all cancers in 2017. The five-year cure rate is only 18% despite great progress in recent diagnosis and treatment. Before diagnosis, lung nodule classification is a key step, especially since automatic classification can help clinicians by providing a valuable opinion. Modern computer vision and machine learning technologies allow very fast and reliable CT image classification. This research area has become very hot for its high efficiency and labor saving. The paper aims to draw a systematic review of the state of the art of automatic classification of lung nodules. This research paper covers published works selected from the Web of Science, IEEEXplore, and DBLP databases up to June 2018. Each paper is critically reviewed based on objective, methodology, research dataset, and performance evaluation. Mainstream algorithms are conveyed and generic structures are summarized. Our work reveals that lung nodule classification based on deep learning becomes dominant for its excellent performance. It is concluded that the consistency of the research objective and integration of data deserves more attention. Moreover, collaborative works among developers, clinicians, and other parties should be strengthened.

Remote sensing image scene classification is a hot research area for its wide applications. More recently, fusion-based methods attract much attention since they are considered to be an useful way for scene feature representation. This paper explores the fusion-based method for remote sensing image scene classification from another viewpoint. First, it is categorized as front side fusion mode, middle side fusion mode, and back side fusion mode. For each fusion mode, the related methods are introduced and described. Then, classification performances of the single side fusion mode and hybrid side fusion mode (combinations of single side fusion) are evaluated. Comprehensive experiments on UC Merced, WHU-RS19, and NWPU-RESISC45 datasets give the comparison result among various fusion methods. The performance comparisons of various modes, and interactions among different fusion modes are also discussed. It is concluded that (1) fusion is an effective way to improve model performance, (2) back side fusion is the most powerful fusion mode, and (3) method with random crop+multiple backbone+average achieves the best performance.

Importance Pediatric palliative care (PPC) is an interdisciplinary collaboration that focuses on the prevention and relief of patient suffering. PPC has emerged as a critical field of medical expertise and practice. However, no information is available regarding the progress of PPC in the Chinese mainland. Objective This study investigated the geographic distribution, team structure, and services of PPC teams in the Chinese mainland. It also investigated the level of understanding and implementation among pediatric oncologists regarding PPC. Methods The PPC subspecialty group of the Pediatrics Society of the Chinese Medical Association included 45 PPC teams. The team structure and services were investigated using questionnaires mailed to the team leader of each PPC team. In addition, we sent questionnaires regarding the level of PPC understanding and implementation of PPC practices to 170 pediatric oncologists in 11 hospitals. Results The geographical distribution of PPC teams is uneven in China. Most PPC teams are concentrated in the eastern provincial capital of China. Most PPC teams had limited staff and services. The level of PPC understanding was considerably limited across all demographics; most pediatric oncologists reported “some understanding” (n = 71, 41.8%) or “poor understanding” (n = 50, 29.4%). Only 62.9% of pediatric oncologists had experience providing advice to family members regarding PPC matters. Interpretation China is currently experiencing a critical shortage of PPC resources. Most pediatric oncologists had a limited understanding of PPC and reported limited practical implementation of PPC, which leads to underutilization of PPC resources.

To investigate the cognitive and psychological outcomes of pediatric allogeneic HSCT survivors in China.A total of 135 3 to 18 years old children and adolescents who underwent allo-HSCT and survived at least 3 months post-HSCT were recruited and completed the assessments. Cognitive and psychological functions were assessed via age-appropriate standardized measures. Clinical information was extracted from the medical records.Forty one 3 to 6 years old children completed Psychological Questionnaires for 3 to 6 years Children. The scores of 21(51.2%) children in cognitive development dimension, 18(43.9%) in motor development dimension, 16(39.0%) in language development and social development dimension, 15(36.6%) in emotion and will dimension and 14(34.1%) in living habits dimension were less than the standard. Fifty six 8 to 16 years old children and adolescents completed the Depression Self-rating Scale for Children and 9 (16.1%) of these met the criteria of depression. Sixty nine 7 to 16 years old children and adolescents completed the screening for Child Anxiety Related Disorders and 7 (10.1%) of these met the criteria of anxiety, especially social phobia and school phobia. Eighty nine 6 to 18 years old children and adolescents completed the Symptom Checklist-90 and 43.8% to 77.5% of these experienced mild symptoms like obsession-compulsion (77.5%), hostility (64%), and interpersonal sensitivity (60.7%). Children treated with total body irradiation (TBI) showed more cognitive impairments like motor deficits than those without TBI. Also older children and adolescents had more symptoms like psychoticism.These findings demonstrated cognitive and psychological late effects of pediatric allo-HSCT survivors in a single center in China and highlighted that the survivors conditioned with TBI had more cognitive impairments and older children and adolescents had more symptoms. Early intervention in these children and adolescents might minimize the cognitive losses and psychological effects.

Unsupervised domain adaptation (UDA) aims at reducing the distribution discrepancy when transferring knowledge from a labeled source domain to an unlabeled target domain. Previous UDA methods assume that the source and target domains share an identical label space, which is unrealistic in practice since the label information of the target domain is agnostic. This paper focuses on a more realistic UDA scenario, i.e. partial domain adaptation (PDA), where the target label space is subsumed to the source label space. In the PDA scenario, the source outliers that are absent in the target domain may be wrongly matched to the target domain (technically named negative transfer), leading to performance degradation of UDA methods. This paper proposes a novel Target Domain Specific Classifier Learning-based Domain Adaptation (TSCDA) method. TSCDA presents a soft-weighed maximum mean discrepancy criterion to partially align feature distributions and alleviate negative transfer. Also, it learns a target-specific classifier for the target domain with pseudo-labels and multiple auxiliary classifiers, to further address classifier shift. A module named Peers Assisted Learning is used to minimize the prediction difference between multiple target-specific classifiers, which makes the classifiers more discriminant for the target domain. Extensive experiments conducted on three PDA benchmark datasets show that TSCDA outperforms other state-of-the-art methods with a large margin, e.g. 4% and 5.6% averagely on Office-31 and Office-Home, respectively.

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The COVID-19 pandemic prominently hit almost all the aspects of our life, especially in routine education. For public health security, online learning has to be enforced to replace classroom learning. Thus, it is a priority to clarify how these changes impacted students. We built a random-effect model of a meta-analysis to pool individual effect sizes for published articles concerning the attitudes and performance towards online learning. Databases included Google Scholar, PubMed and (Chinese) CNKI repository. Further, a moderated analysis and meta-regression were further used to clarify potential heterogenous factors impacting this pooled effect. Forty published papers (n = 98,558) were screened that were eligible for formal analysis. Meta-analytic results demonstrated that 13.3% (95% CI: 10.0–17.5) of students possessed negative attitudes towards online learning during the COVID-19 pandemic. A total of 12.7% (95% CI: 9.6–16.8) students were found to report poor performance in online learning. Moderated analysis revealed poor performance in online learning in the early pandemic (p = 0.006). Results for the meta-regression analysis showed that negative attitudes could predict poor learning performance significantly (p = 0.026). In conclusion, online learning that is caused by COVID-19 pandemic may have brought about negative learning attitudes and poorer learning performance compared to classroom learning, especially in the early pandemic.