Graph Contrastive Multi-view Learning : A Pre-training Framework for Graph Classification

CRediT authorship contribution statement Michael Adjeisah: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Writing – original draft, Visualization. Xinzhong Zhu: Resources, Writing – review & editing, Supervision, Funding acquisition. Huiying Xu: Resources, Supervision. Tewodros Alemu Ayall: Software, Validation, Formal analysis, Data curation.Peer reviewe

Effective Drusen Localization for Early AMD Screening using Sparse Multiple Instance Learning

Age-related Macular Degeneration (AMD) is one of the leading causes of blindness. Automatic screening of AMD has attracted much research effort in recent years because it brings benefits to both patients and ophthalmologists. Drusen is an important clinical indicator for AMD in its early stage. Accurately detecting and localizing drusen are important for AMD detection and grading. In this paper, we propose an effective approach to localize drusen in fundus images. This approach trains a drusen classifier from a weakly labeled dataset, i.e., only the existence of drusen is known but not the exact locations or boundaries, by employing Multiple Instance Learning (MIL). Specifically, considering the sparsity of drusen in fundus images, we employ sparse Multiple Instance Learning to obtain better performance compared with classical MIL. Experiments on 350 fundus images with 96 having AMD demonstrates that on the task of AMD detection, multiple instance learning, both classical and sparse versions, achieve comparable performance compared with fully supervised SVM. On the task of drusen localization, sparse MIL outperforms MIL significantly

KidneyRegNet: A Deep Learning Method for 3DCT-2DUS Kidney Registration during Breathing

This work proposed a novel deep registration pipeline for 3D CT and 2D U/S kidney scans of free breathing, which consists of a feature network, and a 3D-2D CNN-based registration network. The feature network has handcraft texture feature layers to reduce the semantic gap. The registration network is encoder-decoder structure with loss of feature-image-motion (FIM), which enables hierarchical regression at decoder layers and avoids multiple network concatenation. It was first pretrained with retrospective datasets cum training data generation strategy, then adapted to specific patient data under unsupervised one-cycle transfer learning in onsite application. The experiment was on 132 U/S sequences, 39 multiple phase CT and 210 public single phase CT images, and 25 pairs of CT and U/S sequences. It resulted in mean contour distance (MCD) of 0.94 mm between kidneys on CT and U/S images and MCD of 1.15 mm on CT and reference CT images. For datasets with small transformations, it resulted in MCD of 0.82 and 1.02 mm respectively. For large transformations, it resulted in MCD of 1.10 and 1.28 mm respectively. This work addressed difficulties in 3DCT-2DUS kidney registration during free breathing via novel network structures and training strategy.Comment: 15 pages, 8 figures, 9 table

ACHIKO-D350: A dataset for early AMD detection and drusen segmentation

Age related macular degeneration is the third leading cause of global blindness. Its prevalence is increasing in these years for the coming of ”aging population”. Early detection and grading can prevent it from becoming severe and protect vision. Drusen is an important indicator for AMD. Thus automatic drusen detection and segmentation has attracted much research attention in the past years. However, a barrier handicapping the research of drusen segmentation is the lack of a public dataset and test platform. To address this issue, in this paper, we publish a dataset, named ACHIKO-D350, with manually marked drusen boundary. ACHIKO-D350 includes 254 healthy fundus images and 96 fundus images with drusen. The images with drusen cover a wide range of types, including images with sparsely distributed drusen or clumped drusen, images of poor quality, and both well macular centered images and mis-centered images. ACHIKO-D350 will be used for performance evaluation of drusen segmentation methods. It will facilitate an objective evaluation and comparison

Research on school-family-community inter-coupling education model with skill-building approach in China

School-Family-Community Inter-coupling Education Model is to explore and construct an experiential learning mechanism for public benefit education (service learning), with students in the center, on the cornerstone of family education, driven by school education, propped by community education and bonded through service activities, with the aim to open up a pathway for youth education reform suitable for China’s circumstance. Students are the key players in observing, researching and/or identifying public affairs issues and needs from their own perspectives under the guidance of teachers, parents and community leaders, taking initiatives to improve the public affairs around family, school and community through participating, cooperating, serving, reflecting and using their own means. Meanwhile, students can build skills in communicating, cooperating, learning, taking initiatives, serving, and etc. The breakthrough of this experiential education model into the test-oriented school system in China will be realized by developing a series of coursework and a compatible evaluation matrix system in the curricula, easy for administrators and teachers to operate, students to take actions, parents and community leaders to assist, and are adaptable to all grade levels. Our pilot trials and research at primary school levels for the past two years will move up to higher grade levels gradually as the participating students move up

A Microbiome-Based Index for Assessing Skin Health and Treatment Effects for Atopic Dermatitis in Children.

A quantitative and objective indicator for skin health via the microbiome is of great interest for personalized skin care, but differences among skin sites and across human populations can make this goal challenging. A three-city (two Chinese and one American) comparison of skin microbiota from atopic dermatitis (AD) and healthy pediatric cohorts revealed that, although city has the greatest effect size (the skin microbiome can predict the originated city with near 100% accuracy), a microbial index of skin health (MiSH) based on 25 bacterial genera can diagnose AD with 83 to ∼95% accuracy within each city and 86.4% accuracy across cities (area under the concentration-time curve [AUC], 0.90). Moreover, nonlesional skin sites across the bodies of AD-active children (which include shank, arm, popliteal fossa, elbow, antecubital fossa, knee, neck, and axilla) harbor a distinct but lesional state-like microbiome that features relative enrichment of Staphylococcus aureus over healthy individuals, confirming the extension of microbiome dysbiosis across body surface in AD patients. Intriguingly, pretreatment MiSH classifies children with identical AD clinical symptoms into two host types with distinct microbial diversity and treatment effects of corticosteroid therapy. These findings suggest that MiSH has the potential to diagnose AD, assess risk-prone state of skin, and predict treatment response in children across human populations.IMPORTANCE MiSH, which is based on the skin microbiome, can quantitatively assess pediatric skin health across cohorts from distinct countries over large geographic distances. Moreover, the index can identify a risk-prone skin state and compare treatment effect in children, suggesting applications in diagnosis and patient stratification