about
Quality evaluation of microscopy and scanned histological images for diagnostic purposes.Digital pathology imaging as a novel platform for standardization and globalization of quantitative nephropathology.Development of automated quantification methodologies of immunohistochemical markers to determine patterns of immune response in breast cancer: a retrospective cohort study.Automated pollen identification using microscopic imaging and texture analysis.Automatic handling of tissue microarray cores in high-dimensional microscopy images.An automated system for whole microscopic image acquisition and analysis.Sample Selection for Training Cascade Detectors.Influence of Texture and Colour in Breast TMA ClassificationEvaluation of cytokeratin-19 in breast cancer tissue samples: a comparison of automatic and manual evaluations of scanned tissue microarray cylinders.Vascular patterns provide therapeutic targets in aggressive neuroblastic tumorsCADe system integrated within the electronic health recordReview of imaging solutions for integrated quantitative immunohistochemistry in the Pathology daily practice.Eyes of Things.New Trends of Emerging Technologies in Digital Pathology.Breast density classification to reduce false positives in CADe systems.Frequential versus spatial colour textons for breast TMA classification.Her2 Challenge Contest: A Detailed Assessment of Automated Her2 Scoring Algorithms in Whole Slide Images of Breast Cancer Tissues.Teaching Digital Pathology: The International School of Digital Pathology and Proposed Syllabus.Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer.Textural features and SUV-based variables assessed by dual time point 18F-FDG PET/CT in locally advanced breast cancer.Autofocus evaluation for brightfield microscopy pathology.Low-cost oblique illumination: an image quality assessment.Oblique illumination in microscopy: A quantitative evaluation.Automatic quantification of IHC stain in breast TMA using colour analysis.A geodesic deformable model for automatic segmentation of image sequences applied to radiation therapy.Automatic breast parenchymal density classification integrated into a CADe system.Digital Imaging and Communications in Medicine Whole Slide Imaging Connectathon at Digital Pathology Association Pathology Visions 2017.TMA vessel segmentation based on color and morphological features: application to angiogenesis research.Lymph microvascularization as a prognostic indicator in neuroblastoma.A Tree Classifier for Automatic Breast Tissue Classification Based on BIRADS CategoriesFight Recognition in video using Hough Forests and 2D Convolutional Neural NetworkImmune response profile of primary tumour, sentinel and non-sentinel axillary lymph nodes related to metastasis in breast cancer: an immunohistochemical point of viewThe Immune Response in Nonmetastatic Axillary Lymph Nodes Is Associated with the Presence of Axillary Metastasis and Breast Cancer Patient OutcomeOptimum web viewer application for DICOM whole slide image visualization in anatomical pathology
P50
Q31041316-BB115D4F-D10D-42BE-9034-FC70B91AD10BQ33756306-44D19948-3978-4B83-81FB-4901C0602700Q34024612-CD001C8E-8FE5-43B3-B3E3-DAD4F74C82E4Q34440607-BB7C5F08-0D02-4A03-9703-D8F08EE5955DQ35012718-3DA1CD1F-0E8B-4160-8A1B-B46B607A8028Q35185062-6648B073-9F8B-4C8B-8493-79EFF98CAB4AQ35703025-EF92C162-0517-40CD-B8F5-BABD63B6D3E9Q35826025-81797847-1454-4B31-91C4-B2CD65A28469Q35989561-B53AB885-44B5-4743-ACCE-E57581527231Q37190218-69B60B28-16D8-4C0A-84F4-BEAB6518F1C8Q37212255-35FE60DF-FFB4-499D-A113-790A35D9E9F4Q37694078-99BE748A-0CB9-4BA0-9F08-00F69ABE5270Q38771202-1A671534-A88D-4FDA-ACE6-CBC71E9CCCA9Q38813750-10FC1585-7913-4688-A1EF-DEA626AD02ACQ40154001-D47449F5-3B6A-48A6-B816-D00466C4D26CQ40243194-26200C66-877B-4880-8E92-9858F3017785Q40492032-33188397-3FDB-4FCD-B399-5FE99A119546Q40497886-565A8B5C-9135-4A11-812E-C0ABE0D6DA18Q46124450-59BE51F6-7F59-4FC1-B1EA-CB8DD92C3C50Q47140400-1E72C9E4-59E4-408B-8158-46EA4FA32DF5Q48569282-B08CB4DD-B55C-4ECA-BA59-AECF53B65C7EQ49349887-AF242E89-15B8-4CF7-BD81-009BF5B84841Q49598146-47D01F08-9B86-43BC-8262-2469AE8194EEQ50562085-F7583718-07EC-4EE4-A060-C0E318D58DF7Q50698006-50B42D95-E68D-4656-9010-41EF96BA1010Q51673516-48D1EDA9-1012-4510-B098-A15DE8629C22Q52605951-4FCC564B-2716-44FF-9BCD-10D297A944ADQ54318062-6D992AAC-CF19-41C3-89A8-D05B100BA3ACQ55241229-5B714B95-8682-4A83-BDEA-063F6DBDD265Q56984327-DBE318FC-4CAA-4423-9AB2-4EFDD479F3DAQ89558391-5A28A8B1-8109-45FE-9416-7CA4519B930EQ91842936-480194E8-37F0-4989-BF71-FF88E80F2DE1Q92184781-DB84C74A-51D0-4E81-A889-28757BB40339Q92828517-34EF22CB-B248-40D3-A73F-5BA33C3A440F
P50
description
hulumtuese
@sq
onderzoeker
@nl
researcher
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հետազոտող
@hy
name
Gloria Bueno
@ast
Gloria Bueno
@en
Gloria Bueno
@es
Gloria Bueno
@nl
Gloria Bueno
@sl
type
label
Gloria Bueno
@ast
Gloria Bueno
@en
Gloria Bueno
@es
Gloria Bueno
@nl
Gloria Bueno
@sl
prefLabel
Gloria Bueno
@ast
Gloria Bueno
@en
Gloria Bueno
@es
Gloria Bueno
@nl
Gloria Bueno
@sl
P106
P1153
7003988757
P21
P2456
P31
P496
0000-0002-7345-4869