Medical Optical Imaging and Virtual Microscopy Image Analysis : First International Workshop, MOVI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings / edited by Yuankai Huo, Bryan A. Millis, Yuyin Zhou, Xiangxue Wang, Adam P. Harrison, Ziyue Xu.

Format:

Book

Contributor:

Huo, Yuankai, editor.

Series:

Lecture Notes in Computer Science, 1611-3349 ; 13578

Language:

English

Subjects (All):

Image processing--Digital techniques.

Image processing.

Computer vision.

Artificial intelligence.

Education--Data processing.

Education.

Application software.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Artificial Intelligence.

Computers and Education.

Computer and Information Systems Applications.

Local Subjects:

Computer Imaging, Vision, Pattern Recognition and Graphics.

Artificial Intelligence.

Computers and Education.

Computer and Information Systems Applications.

Physical Description:

1 online resource (200 pages)

Edition:

1st ed. 2022.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2022.

System Details:

Mode of access: World Wide Web.

Summary:

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.

Contents:

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.

Notes:

Includes bibliographical references and index.

Other Format:

Print version: Huo, Yuankai Medical Optical Imaging and Virtual Microscopy Image Analysis

ISBN:

9783031169618

3031169611