Medical Optical Imaging and Virtual Microscopy Image Analysis: First International Workshop, MOVI 2022, Held in Conjunction with MICCAI 2022, Singapor

ISBN-13: 9783031169601 / Angielski / Miękka / 2022 / 204 str.

Yuankai Huo;Bryan A. Millis;Yuyin Zhou

Opis

This book constitutes the refereed proceedings of the 1st International Workshop on Medical Optical Imaging and Virtual Microscopy Image Analysis, MOVI 2022, held in conjunction with the 25th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2022, in Singapore, Singapore, in September 2022. The 18 papers presented at MOVI 2022 were carefully reviewed and selected from 25 submissions. The objective of the MOVI workshop is to promote novel scalable and resource-efficient medical image analysis algorithms for high-dimensional image data analy-sis, from optical imaging to virtual microscopy.

Spis treści

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.