Imaging Informatics Division
Deep Learning Based Plant Disease Classification With Explainable AI and Mitigation Recommendation
Plants show visible symptoms of getting infected with a disease. Presently an experienced plant pathologist can diagnose the condition through visual inspection of disease-affected plants. However, manual visualization is time-consuming and depends on the plant pathologist's expertise in identifying plant disease.
ReadRegional registration of whole slide image stacks containing major histological artifacts
Background High resolution 2D whole slide imaging provides rich information about the tissue structure. This information can be a lot richer if these 2D images can be stacked into a 3D tissue volume. A 3D analysis, however, requires accurate reconstruction of the tissue volume from the 2D image stack. This task is not trivial due to the distortions such as tissue tearing, folding and missing at each slide. Performing registration for the whole tissue slices may be adversely affected by distorted tissue regions. Consequently, regional registration is found to be more effective. In this paper, we propose a new approach to an accurate and robust registration of regions of interest for whole slide images. We introduce the idea of multi-scale attention for registration.
ReadAn end-to-end breast tumour classification model using context-based patch modelling - A BiLSTM approach for image classification
Researchers working on computational analysis of Whole Slide Images (WSIs) in histopathology have primarily resorted to patch-based modelling due to large resolution of each WSI. The large resolution makes WSIs infeasible to be fed directly into the machine learning models due to computational constraints. However, due to patch-based analysis, most of the current methods fail to exploit the underlying spatial relationship among the patches.
ReadExploring MRI based radiomics analysis of intratumoral spatial heterogeneity in locally advanced nasopharyngeal carcinoma treated with intensity modulated radiotherapy
We hypothesized that spatial heterogeneity exists between recurrent and non-recurrent regions within a tumor. The aim of this study was to determine if there is a difference between radiomics features derived from recurrent versus non recurrent regions within the tumor based on pre-treatment MRI.
ReadCellular ageing of oral fibroblasts differentially modulates extracellular matrix organization
Ageing is associated with an impaired cellular function that can affect tissue architecture and wound healing in gingival and periodontal tissues. However, the impact of oral fibroblast ageing on the structural organization of the extracellular matrix (ECM) proteins is poorly understood. Hence, in this study, we investigated the impact of cellular ageing of oral fibroblasts on the production and structural organization of collagen and other ECM proteins.
ReadVirtual screening of potentially endocrine-disrupting chemicals against nuclear receptors and its application to identify PPARγ-bound fatty acids
Nuclear receptors (NRs) are key regulators of energy homeostasis, body development, and sexual reproduction. Xenobiotics binding to NRs may disrupt natural hormonal systems and induce undesired adverse effects in the body. However, many chemicals of concerns have limited or no experimental data on their potential or lack-of-potential endocrine-disrupting effects.
ReadAccelerated spin dynamics using deep learning corrections
Theoretical models capture very precisely the behaviour of magnetic materials at the microscopic level. This makes computer simulations of magnetic materials, such as spin dynamics simulations, accurately mimic experimental results.
ReadMachine-learning study using improved correlation configuration and application to quantum Monte Carlo simulation
We use the Fortuin-Kasteleyn representation-based improved estimator of the correlation configuration as an alternative to the ordinary correlation configuration in the machine-learning study of the phase classification of spin models.
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