Medical optical imaging and virtual ...
MOVI (Workshop) (2022 :)

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  • Medical optical imaging and virtual microscopy image analysis = first International Workshop, MOVI 2022, held in conjunction with MICCAI 2022, Singapore, September 18, 2022 : proceedings /
  • 紀錄類型: 書目-電子資源 : Monograph/item
    正題名/作者: Medical optical imaging and virtual microscopy image analysis/ edited by Yuankai Huo ... [et al.].
    其他題名: first International Workshop, MOVI 2022, held in conjunction with MICCAI 2022, Singapore, September 18, 2022 : proceedings /
    其他題名: MOVI 2022
    其他作者: Huo, Yuankai.
    團體作者: MOVI (Workshop)
    出版者: Cham :Springer Nature Switzerland : : 2022.,
    面頁冊數: xi, 190 p. :ill. (some col.), digital ;24 cm.
    內容註: Cell counting with inverse distance kernel and self-supervised learning -- Predicting the visual attention of pathologists evaluating whole slide images of cancer -- Edge-Based Self-Supervision for Semi-Supervised Few-Shot Microscopy Image Cell Segmentation -- Joint Denoising and Super-resolution for Fluorescence Microscopy using Weakly-supervised Deep Learning -- MxIF Q-score: Biology-informed Quality Assurance for Multiplexed Immunofluorescence Imaging -- A Pathologist-Informed Workflow for Classification of Prostate Glands in Histopathology -- Leukocyte Classification using Multimodal Architecture Enhanced by Knowledge Distillation -- Deep learning on lossily compressed pathology images: adverse effects for ImageNet pre-trained models -- Profiling DNA damage in 3D Histology Samples -- Few-shot segmentation of microscopy images using Gaussian process -- Adversarial Stain Transfer to Study the Effect of Color Variation on Cell Instance Segmentation -- Constrained self-supervised method with temporal ensembling for fiber bundle detection on anatomic tracing data -- Sequential multi-task learning for histopathology-based prediction of genetic mutations with extremely imbalanced labels -- Morph-Net: End-to-End Prediction of Nuclear Morphological Features from Histology Images -- A Light-weight Interpretable Model for Nuclei Detection and Weakly-supervised Segmentation -- A coarse-to-fine segmentation methodology based on deep networks for automated analysis of Cryptosporidium parasite from fluorescence microscopic images -- Swin Faster R-CNN for Senescence Detection of Mesenchymal Stem Cells in Bright-field Images -- Characterizing Continual Learning Scenarios for Tumor Classification in Histopathology Images.
    Contained By: Springer Nature eBook
    標題: Diagnostic imaging - Congresses. - Data processing -
    電子資源: https://doi.org/10.1007/978-3-031-16961-8
    ISBN: 9783031169618
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