Medical Optical Imaging and Virtual Microscopy Image Analysis
portes grátis
Medical Optical Imaging and Virtual Microscopy Image Analysis
First International Workshop, MOVI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings
Huo, Yuankai; Harrison, Adam P.; Xu, Ziyue; Millis, Bryan A.; Wang, Xiangxue; Zhou, Yuyin
Springer International Publishing AG
09/2022
190
Mole
Inglês
9783031169601
15 a 20 dias
320
Descrição não disponível.
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.
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artificial intelligence;color image processing;color images;computer networks;computer systems;computer vision;deep learning;image analysis;image matching;image processing;image quality;image segmentation;machine learning;neural networks;pattern recognition;reference image
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.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
