Graph matching survey for medical imaging: On the way to deep learning

Graph-matching distance between individuals' functional connectomes varies with relatedness, age, and cognitive score

Functional connectomes (FCs), represented by networks or graphs that summarize coactivation patterns between pairs of brain regions, have been related at a population level to age, sex, cognitive/behavioral scores, life experience, genetics, and disease/disorders. However, quantifying FC differences between individuals also provides a rich source of information with which to map to differences in those individuals' biology, experience, genetics or behavior. In this study, graph matching is used to create a novel inter-individual FC metric, called swap distance, that quantifies the distance between pairs of individuals' partial FCs, with a smaller swap distance indicating the individuals have more similar FC. We apply graph matching to align FCs between individuals from the the Human Connectome ProjectN = 997 and find that swap distance (i) increases with increasing familial distance, (ii) increases with subjects' ages, (iii) is smaller for pairs of females compared to pairs of males, and (iv) is larger for females with lower cognitive scores compared to females with larger cognitive scores. Regions that contributed most to individuals' swap distances were in higher-order networks, that is, default-mode and fronto-parietal, that underlie executive function and memory. These higher-order networks' regions also had swap frequencies that varied monotonically with familial relatedness of the individuals in question. We posit that the proposed graph matching technique provides a novel way to study inter-subject differences in FC and enables quantification of how FC may vary with age, relatedness, sex, and behavior.

Application of hyperspectral and deep learning in farmland soil microplastic detection

The ecological environment is gravely threatened by the buildup of microplastics (MPs) in soil. Currently, there are no established techniques for detecting MPs in soil. Some of the standard chemical detection methods now in use are time-consuming and cumbersome. This research suggested a method for identifying soil microplastic polymers (MPPs) based on convolutional neural networks (CNN) and hyperspectral imaging (HSI) technologies to address this issue. The categorization model for MPPs on the soil surface was first established by simulating the natural soil environment in the lab. While decision tree (DT) and support vector machine (SVM) models' classification accuracy was 87.9 % and 85.6 %, respectively, that of CNN was 92.6 %. The HIS and CNN model combination produced the best classification results out of all of these models. Secondly, farmland in Guangzhou's Tianhe, Panyu, and Zengcheng districts was sampled for surface soil samples measuring 0-20 cm in order to confirm the model's accuracy in the actual environment. Before data analysis, the physicochemical properties of soil samples were determined by a standardization scheme. MPs in soil samples were extracted by traditional chemical detection method and their chemical properties were obtained as the results of the control group. Then, CNN was applied to hyperspectral data from soil samples collected for MPs detection. Finally, it was demonstrated that the physical and chemical properties of the soil have an impact on the accuracy of the model through the investigation of the physical and chemical characteristics of soil samples from three distinct areas. On the other hand, the results indicated that the suggested technique offers quick and non-destructive results for MPPs detection when comparing the detection results of hyperspectral and conventional chemical methods.