Medical Optical Imaging and Virtual Microscopy Image Analysis First International Workshop MOVI 2022 Held in Conjunction with MICCAI 2022 Proceedings 1st edition by Yuankai Huo, Bryan Millis, Yuyin Zhou, Xiangxue Wang, Adam Harrison, Ziyue Xu 9783031169601 3031169603

Medical Optical Imaging and Virtual Microscopy Image Analysis First International Workshop MOVI 2022 Held in Conjunction with MICCAI 2022 Proceedings Yuankai Huo – Ebook Instant Download/Delivery ISBN(s): 9783031169601,3031169603

Product details:

• ISBN 10: 3031169603
• ISBN 13: 9783031169601
• Author: Yuankai, Bryan, Yuyin, Xiangxue, Adam, Ziyue

Table Contents:

1. Cover
2. Front Matter
3. Cell Counting with Inverse Distance Kernel and Self-supervised Learning
4. Predicting the Visual Attention of Pathologists Evaluating Whole Slide Images of Cancer
5. Edge-Based Self-supervision for Semi-supervised Few-Shot Microscopy Image Cell Segmentation
6. Joint Denoising and Super-Resolution for Fluorescence Microscopy Using Weakly-Supervised Deep Learning
7. MxIF Q-score: Biology-Informed Quality Assurance for Multiplexed Immunofluorescence Imaging
8. A Pathologist-Informed Workflow for Classification of Prostate Glands in Histopathology
9. Leukocyte Classification Using Multimodal Architecture Enhanced by Knowledge Distillation
10. Deep Learning on Lossily Compressed Pathology Images: Adverse Effects for ImageNet Pre-trained Models
11. Profiling DNA Damage in 3D Histology Samples
12. Few-Shot Segmentation of Microscopy Images Using Gaussian Process
13. Adversarial Stain Transfer to Study the Effect of Color Variation on Cell Instance Segmentation
14. Constrained Self-supervised Method with Temporal Ensembling for Fiber Bundle Detection on Anatomic Tracing Data
15. Sequential Multi-task Learning for Histopathology-Based Prediction of Genetic Mutations with Extremely Imbalanced Labels
16. Morph-Net: End-to-End Prediction of Nuclear Morphological Features from Histology Images
17. A Light-Weight Interpretable Model for Nuclei Detection and Weakly-Supervised Segmentation
18. A Coarse-to-Fine Segmentation Methodology Based on Deep Networks for Automated Analysis of Cryptosporidium Parasite from Fluorescence Microscopic Images
19. Swin Faster R-CNN for Senescence Detection of Mesenchymal Stem Cells in Bright-Field Images
20. Characterizing Continual Learning Scenarios for Tumor Classification in Histopathology Images
21. Back Matter

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