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Kategorie szczegółowe BISAC

Medical Image Computing and Computer Assisted Intervention - Miccai 2020: 23rd International Conference, Lima, Peru, October 4-8, 2020, Proceedings, P

ISBN-13: 9783030597214 / Angielski / Miękka / 2020 / 811 str.

Anne L. Martel; Purang Abolmaesumi; Danail Stoyanov
Medical Image Computing and Computer Assisted Intervention - Miccai 2020: 23rd International Conference, Lima, Peru, October 4-8, 2020, Proceedings, P Anne L. Martel Purang Abolmaesumi Danail Stoyanov 9783030597214 Springer - książkaWidoczna okładka, to zdjęcie poglądowe, a rzeczywista szata graficzna może różnić się od prezentowanej.

Medical Image Computing and Computer Assisted Intervention - Miccai 2020: 23rd International Conference, Lima, Peru, October 4-8, 2020, Proceedings, P

ISBN-13: 9783030597214 / Angielski / Miękka / 2020 / 811 str.

Anne L. Martel; Purang Abolmaesumi; Danail Stoyanov
cena 401,58
(netto: 382,46 VAT:  5%)

Najniższa cena z 30 dni: 385,52
Termin realizacji zamówienia:
ok. 22 dni roboczych.

Darmowa dostawa!
Kategorie:
Informatyka
Kategorie BISAC:
Computers > Software Development & Engineering - Computer Graphics
Computers > Artificial Intelligence - Computer Vision & Pattern Recognition
Computers > Data Science - General
Wydawca:
Springer
Język:
Angielski
ISBN-13:
9783030597214
Rok wydania:
2020
Wydanie:
2020
Ilość stron:
811
Waga:
1.16 kg
Wymiary:
23.39 x 15.6 x 4.29
Oprawa:
Miękka
Wolumenów:
01
Dodatkowe informacje:
Komentarz
Wydanie ilustrowane

Biological, Optical, Microscopic Imaging.- Channel Embedding for Informative Protein Identification from Highly Multiplexed Images.- Demixing Calcium Imaging Data in C. elegans via Deformable Non-negative Matrix Factorization.- Automated Measurements of Key Morphological Features of Human Embryos for IVF.- A Novel Approach to Tongue Standardization and Feature Extraction.- Patch-based Non-Local Bayesian Networks for Blind Confocal Microscopy Denoising.- Attention-guided Quality Assessment for Automated Cryo-EM Grid Screening.- MitoEM Dataset: Large-scale 3D Mitochondria Instance Segmentation from EM Images.- Learning Guided Electron Microscopy with Active Acquisition.- Neuronal Subcompartment Classification and Merge Error Correction.- Microtubule Tracking in Electron Microscopy Volumes.- Leveraging Tools from Autonomous Navigation for Rapid, Robust Neuron Connectivity.- Statistical Atlas of C.elegans Neurons.- Probabilistic Segmentation and Labeling of C. elegans Neurons.- Segmenting Continuous but Sparsely-Labeled Structures in Super-Resolution Microscopy Using Perceptual Grouping.- DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging.- Isotropic Reconstruction of 3D EM Images with Unsupervised Degradation Learning.- Background and illumination correction for time-lapse microscopy data  with correlated foreground.- Joint Spatial-Wavelet Dual-Stream Network for Super-Resolution.- Towards Neuron Segmentation from Macaque Brain Images: A Weakly Supervised Approach.- 3D Reconstruction and Segmentation of Dissection Photographs for MRI-free Neuropathology.- DistNet: Deep Tracking by displacement regression: application to bacteria growing in the Mother Machine.- A weakly supervised deep learning approach for detecting malaria and sickle cell anemia in blood films.- Imaging Scattering Characteristics of Tissue in Transmitted Microscopy.- Attention based multiple instance learning for classification of blood cell disorders.- A generative modeling approach for interpreting population-level variability in brain structure.- Processing-Aware Real-Time Rendering for Optimized Tissue Visualization in Intraoperative 4D OCT.- Cell Segmentation and Stain Normalization.- Boundary-assisted Region Proposal Networks for Nucleus Segmentation.- BCData: A Large-Scale Dataset and Benchmark for Cell Detection and Counting.- Weakly-Supervised Nucleus Segmentation Based on Point Annotations: A Coarse-to-Fine Self-Stimulated Learning Strategy.- Structure Preserving Stain Normalization of Histopathology Images Using Self Supervised Semantic Guidance.- A Novel Loss Calibration Strategy for Object Detection Networks Training on Sparsely Annotated Pathological Datasets.- Histopathological Stain Transfer Using Style Transfer Network With Adversarial Loss.- Instance-aware Self-supervised Learning for Nuclei Segmentation.- StyPath: Style-Transfer Data Augmentation For Robust Histology Image Classification.- Multimarginal Wasserstein Barycenter for Stain Normalization and Augmentation.- Corruption-Robust Enhancement of Deep Neural Networks for Classification of Peripheral Blood Smear Images.- Multi-Field of View Aggregation and Context Encoding for Single-Stage Nucleus Recognition.- Self-Supervised Nuclei Segmentation in Histopathological Images Using Attention.- FocusLiteNN: High Efficiency Focus Quality Assessment for Digital Pathology.- Histopathology Image Analysis.- Pairwise Relation Learning for Semi-supervised Gland Segmentation.- Ranking-Based Survival Prediction on Histopathological Whole-Slide Images.- Renal Cell Carcinoma Detection and Subtyping with Minimal Point-Based Annotation in Whole-Slide Images.- Censoring-Aware Deep Ordinal Regression for Survival Prediction from Pathological Images.- Tracing Diagnosis Paths on Histopathology WSIs for Diagnostically Relevant Case Recommendation.- Weakly supervised multiple instance learning histopathological tumor segmentation.- Divide-and-Rule: Self-Supervised Learning for Survival Analysis in Colorectal Cancer.- Microscopic fine-grained instance classification through deep attention.- A Deformable CRF Model for Histopathology Whole-slide Image Classification.- Deep Active Learning for Breast Cancer Segmentation on Immunohistochemistry Images.- Multiple Instance Learning with Center Embeddings for Histopathology Classification.- Graph Attention Multi-instance Learning for Accurate Colorectal Cancer Staging.- Deep Interactive Learning: An Efficient Labeling Approach for Deep Learning-Based Osteosarcoma Treatment Response Assessment.- Modeling Histological Patterns for Differential Diagnosis of Atypical Breast Lesions.- Foveation for Segmentation of Mega-pixel Histology Images.- Multimodal Latent Semantic Alignment for Automated Prostate Tissue Classification and Retrieval.- Opthalmology.- GREEN: a Graph REsidual rE-ranking Network for Grading Diabetic Retinopathy.- Combining Fundus Images and Fluorescein Angiography for Artery/Vein Classification Using the Hierarchical Vessel Graph Network.- Adaptive Dictionary Learning Based Multimodal Branch Retinal Vein Occlusion Fusion.- TR-GAN: Topology Ranking GAN with Triplet Loss for Retinal Artery/Vein Classification.- DeepGF: Glaucoma Forecast Using Sequential Fundus Images.- Single-Shot Retinal Image Enhancement Using Deep Image Prior.- Robust Layer Segmentation against Complex Retinal Abnormalities for en face OCTA Generation.- Anterior Segment Eye Lesion Segmentation with Advanced Fusion Strategies and Auxiliary Tasks.- Cost-Sensitive Regularization for Diabetic Retinopathy Grading from Eye Fundus Images.- Disentanglement Network for Unpsupervised Speckle Reduction of Optical Coherence Tomography Images.- Positive-Aware Lesion Detection Network with Cross-scale Feature Pyramid for OCT Images.- Retinal Layer Segmentation Reformulated as OCT Language Processing.- Reconstruction and Quantification of 3D Iris Surface for Angle-Closure Glaucoma Detection in Anterior Segment OCT.- Open-Appositional-Synechial Anterior Chamber Angle Classification in AS-OCT Sequences.- A Macro-Micro Weakly-supervised Framework for AS-OCT Tissue Segmentation.- Macular Hole and Cystoid Macular Edema Joint Segmentation by Two-Stage Network and Entropy Minimization.- Retinal Nerve Fiber Layer Defect Detection With Position Guidance.- An Elastic Interaction Based-Loss Function for Medical Image Segmentation.- Retinal Image Segmentation with a Structure-Texture Demixing Network.- BEFD: Boundary Enhancement and Feature Denoising for Vessel Segmentation.- Boosting Connectivity in Retinal Vessel Segmentation via a Recursive Semantics-Guided Network.- RVSeg-Net: an Efficient Feature Pyramid Cascade Network for Retinal Vessel Segmentation-



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