Computational Pathology and Ophthalmic Medical Image Analysis : First International Workshop, COMPAY 2018, and 5th International Workshop, OMIA 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16 - 20, 2018, Proceedings / edited by Danail Stoyanov, Zeike Taylor, Francesco Ciompi, Yanwu Xu, Anne Martel, Lena Maier-Hein, Nasir Rajpoot, Jeroen van der Laak, Mitko Veta, Stephen McKenna, David Snead, Emanuele Trucco, Mona K. Garvin, Xin Jan Chen, Hrvoje Bogunovic.

Format:

Book

Contributor:

Stoyanov, Danail, Editor.

Taylor, Zeike, Editor.

Ciompi, Francesco, Editor.

Xu, Yanwu, Editor.

Martel, Anne, Editor.

Maier-Hein, Lena., Editor.

Rajpoot, Nasir., Editor.

van der Laak, Jeroen., Editor.

Veta, Mitko, Editor.

McKenna, Stephen, Editor.

Snead, David., Editor.

Trucco, Emanuele, Editor.

Garvin, Mona K., Editor.

Chen, Xin Jan., Editor.

Bogunovic, Hrvoje., Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 11039

Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 11039

Language:

English

Subjects (All):

Computer vision.

Artificial intelligence.

Computer arithmetic and logic units.

Computer science-Mathematics.

Mathematical statistics.

Pattern recognition systems.

Computer Vision.

Artificial Intelligence.

Arithmetic and Logic Structures.

Probability and Statistics in Computer Science.

Automated Pattern Recognition.

Local Subjects:

Computer Vision.

Artificial Intelligence.

Arithmetic and Logic Structures.

Probability and Statistics in Computer Science.

Automated Pattern Recognition.

Physical Description:

1 online resource (XVII, 347 pages) : 135 illustrations

Edition:

1st ed. 2018.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2018.

System Details:

text file PDF

Summary:

This book constitutes the refereed joint proceedings of the First International Workshop on Computational Pathology, COMPAY 2018, and the 5th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 19 full papers (out of 25 submissions) presented at COMPAY 2018 and the 21 full papers (out of 31 submissions) presented at OMIA 2018 were carefully reviewed and selected. The COMPAY papers focus on artificial intelligence and deep learning. The OMIA papers cover various topics in the field of ophthalmic image analysis.

Contents:

Improving Accuracy of Nuclei Segmentation by Reducing Histological Image Variability

Multi-Resolution Networks for Semantic Segmentation in Whole Slide Images

Improving High Resolution Histology Image Classification with Deep Spatial Fusion Network

Construction of a Generative Model of H&E Stained Pathology Images of Pancreas Tumors Conditioned by a Voxel Value of MRI Image

Accurate 3D reconstruction of a whole pancreatic cancer tumor from pathology images with different stains

Role of Task Complexity and Training in Crowdsourced Image Annotation

Capturing global spatial context for accurate cell classification in skin cancer histology

Exploiting Multiple Color Representations to Improve Colon Cancer Detection in Whole Slide H&E Stains

Leveraging Unlabeled Whole-Slide-Images for Mitosis Detection

Evaluating Out-of-the-box Methods for the Classification of Hematopoietic Cells in Images of Stained Bone Marrow

DeepCerv: Deep neural network for segmentation free robust cervical cell classification

Whole slide image registration for the study of tumor heterogeneity

Modality Conversion from Pathological Image to Ultrasonic Image Using Convolutional Neural Network

Structure instance segmentation in renal tissue: a case study on tubular immune cell detection

Cellular Community Detection for Tissue Phenotyping in Histology Images

Automatic Detection of Tumor Budding in Colorectal Carcinoma with Deep Learning

Significance of Hyperparameter Optimization for Metastasis Detection in Breast Histology Images

Image Magnification Regression Using DenseNet for Exploiting Histopathology Open Access Content

Uncertainty Driven Pooling Network for Microvessel Segmentation in Routine Histology Images

Ocular Structures Segmentation from Multi-sequences MRI using 3D Unet with Fully Connected CRFs

Classification of Findings with Localized Lesions in Fundoscopic Images using a Regionally Guided CNN

Segmentation of Corneal Nerves Using a U-Net-based Convolutional Neural Network

Automatic Pigmentation Grading of the Trabecular Meshwork in Gonioscopic Images

Large Receptive Field Fully Convolutional Network for Semantic Segmentation of Retinal Vasculature in Fundus Images

Explaining Convolutional Neural Networks for Area Estimation of Choroidal Neovascularization via Genetic Programming

Joint Segmentation and Uncertainty Visualization of Retinal Layers in Optical Coherence Tomography Images using Bayesian Deep Learning

cGAN-based lacquer cracks segmentation in ICGA image

Localizing Optic Disc and Cup for Glaucoma Screening via Deep Object Detection Networks

Fundus Image Quality-guided Diabetic Retinopathy Grading

DeepDisc: Optic Disc Segmentation based on Atrous Convolution and Spatial Pyramid Pooling

Large-scale Left and Right Eye Classification in Retinal Images

Automatic Segmentation of Cortex and Nucleus in Anterior Segment OCT Images

Local Estimation of the Degree of Optic Disc Swelling from Color Fundus Photography

Visual Field based Automatic Diagnosis of Glaucoma Using Deep Convolutional Neural Network

Towards standardization of retinal vascular measurements: on the effect of image centering

Feasibility study of Subfoveal Choroidal Thickness Changes in Spectral-Domain Optical Coherence Tomography Measurements of Macular Telangiectasia Type 2

Segmentation of retinal layers in OCT images of the mouse eye utilizing polarization contrast

Glaucoma Diagnosis from Eye Fundus Images Based on Deep Morphometric Feature Estimation

2D Modeling and Correction of Fan-beam Scan Geometry in OCT

A Bottom-up Saliency Estimation Approach for Neonatal Retinal Images.

Other Format:

Printed edition:

ISBN:

978-3-030-00949-6

9783030009496

Access Restriction:

Restricted for use by site license.