Browsing by Author "Lee, M"

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    Corrosion performance of Ni-based structural alloys for applications in molten-salt based energy systems: experiment & numerical validation
    (Elsevier, 2021-09) Lee, M; Muránsky, O; Karatchevtseva, I; Huang, HF; Laws, KJ
    The molten salt corrosion performance of a Y2O3-strengthened Ni-Cr alloy (MA754®) designed for high temperature applications (> 750 °C) was compared to purpose-designed Ni-Mo-Cr molten-salt resistant alloys (GH3535, HASTELLOY-N®). The significant material mass loss of MA754® alloy is attributed to its higher Cr-content. However, Y2O3 dispersoids are shown to play only a minor role in the corrosion performance of this oxide-dispersion-strengthened (ODS) alloy. The current result, thus, points to the possibility for the development of low Cr-content ODS alloys that combines the high-temperature properties of ODS MA754® alloy with good molten salt corrosion resistance of well-established GH3535 and HASTELLOY-N® alloys. Crown Copyright © 2021 Published by Elsevier Ltd
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    PathoFusion: an open-source AI framework for recognition of pathomorphological features and mapping of immunohistochemical data
    (MDPI, 2021-02-04) Bao, GQ; Wang, XY; Xu, R; Loh, C; Adeyinka, OD; Pieris, DA; Cherepanoff, S; Gracie, G; Lee, M; McDonald, KL; Nowak, AK; Banati, RB; Buckland, ME; Graeber, MB
    We have developed a platform, termed PathoFusion, which is an integrated system for marking, training, and recognition of pathological features in whole-slide tissue sections. The platform uses a bifocal convolutional neural network (BCNN) which is designed to simultaneously capture both index and contextual feature information from shorter and longer image tiles, respectively. This is analogous to how a microscopist in pathology works, identifying a cancerous morphological feature in the tissue context using first a narrow and then a wider focus, hence bifocal. Adjacent tissue sections obtained from glioblastoma cases were processed for hematoxylin and eosin (H&E) and immunohistochemical (CD276) staining. Image tiles cropped from the digitized images based on markings made by a consultant neuropathologist were used to train the BCNN. PathoFusion demonstrated its ability to recognize malignant neuropathological features autonomously and map immunohistochemical data simultaneously. Our experiments show that PathoFusion achieved areas under the curve (AUCs) of 0.985 ± 0.011 and 0.988 ± 0.001 in patch-level recognition of six typical pathomorphological features and detection of associated immunoreactivity, respectively. On this basis, the system further correlated CD276 immunoreactivity to abnormal tumor vasculature. Corresponding feature distributions and overlaps were visualized by heatmaps, permitting high-resolution qualitative as well as quantitative morphological analyses for entire histological slides. Recognition of more user-defined pathomorphological features can be added to the system and included in future tissue analyses. Integration of PathoFusion with the day-to-day service workflow of a (neuro)pathology department is a goal. The software code for PathoFusion is made publicly available. © 2021 by the Authors.