000 05752nam a22006255i 4500
001 978-3-031-33658-4
003 DE-He213
005 20240207153607.0
007 cr nn 008mamaa
008 230529s2023 sz | s |||| 0|eng d
020 _a9783031336584
_9978-3-031-33658-4
050 4 _aTA1501-1820
050 4 _aTA1634
072 7 _aUYT
_2bicssc
072 7 _aCOM016000
_2bisacsh
072 7 _aUYT
_2thema
082 0 4 _a006
_223
245 1 0 _aMitosis Domain Generalization and Diabetic Retinopathy Analysis
_h[electronic resource] :
_bMICCAI Challenges MIDOG 2022 and DRAC 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18-22, 2022, Proceedings /
_cedited by Bin Sheng, Marc Aubreville.
250 _a1st ed. 2023.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2023.
300 _aIX, 242 p. 85 illus., 60 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Computer Science,
_x1611-3349 ;
_v13597
500 _aAcceso multiusuario
505 0 _aPreface DRAC 2022 -- nnU-Net Pre- and Postprocessing Strategies for UW-OCTA Segmentation Tasks in Diabetic Retinopathy Analysis -- Automated analysis of diabetic retinopathy using vessel segmentation maps as inductive bias -- Bag of Tricks for Diabetic Retinopathy Grading of Ultra-wide Optical Coherence Tomography Angiography Images -- Deep convolutional neural network for image quality assessment and diabetic retinopathy grading -- Diabetic Retinal Overlap Lesion Segmentation Network -- An Ensemble Method to Automatically Grade Diabetic Retinopathy with Optical Coherence Tomography Angiography Images -- Bag of Tricks for Developing Diabetic Retinopathy Analysis Framework to Overcome Data Scarcity -- Deep-OCTA: Ensemble Deep Learning Approaches for Diabetic Retinopathy Analysis on OCTA Images -- Deep Learning-based Multi-tasking System for Diabetic Retinopathy in UW-OCTA images -- Semi-Supervised Semantic Segmentation Methods for UW-OCTA Diabetic Retinopathy Grade Assessment -- Image Quality Assessment based on Multi-Model Ensemble Class-Imbalance Repair Algorithm for Diabetic Retinopathy UW-OCTA Images -- An improved U-Net for diabetic retinopathy segmentation -- A Vision transformer based deep learning architecture for automatic diagnosis of diabetic retinopathy in optical coherence tomography angiography -- Segmentation, Classification, and Quality Assessment of UW-OCTA Images for the Diagnosis of Diabetic Retinopathy -- Data Augmentation by Fourier Transformation for Class-Imbalance : Application to Medical Image Quality Assessment -- Automatic image quality assessment and DR grading method based on convolutional neural network -- A transfer learning based model ensemble method for image quality assessment and diabetic retinopathy grading -- Automatic Diabetic Retinopathy Lesion Segmentation in UW-OCTA Images using Transfer Learning -- Preface MIDOG 2022 -- Reference Algorithms for the Mitosis Domain Generalization (MIDOG) 2022 Challenge -- Radial Prediction Domain Adaption Classifier for the MIDOG 2022 challenge -- Detecting Mitoses with a Convolutional Neural Network for MIDOG 2022 Challenge -- Tackling Mitosis Domain Generalization in Histopathology Images with Color Normalization -- "A Deep Learning based Ensemble Model for Generalized Mitosis Detection in H&E stained Whole Slide Images" -- Fine-Grained Hard-Negative Mining: Generalizing Mitosis Detection with a Fifth of the MIDOG 2022 Dataset -- Multi-task RetinaNet for mitosis detection. .
520 _aThis book constitutes two challenges that were held in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, which took place in Singapore during September 18-22, 2022. The peer-reviewed 20 long and 5 short papers included in this volume stem from the following three biomedical image analysis challenges: Mitosis Domain Generalization Challenge (MIDOG 2022), Diabetic Retinopathy Analysis Challenge (CRAC 2022) The challenges share the need for developing and fairly evaluating algorithms that increase accuracy, reproducibility and efficiency of automated image analysis in clinically relevant applications.
541 _fUABC ;
_cPerpetuidad
650 0 _aImage processing
_xDigital techniques.
650 0 _aComputer vision.
650 0 _aComputers.
650 0 _aApplication software.
650 0 _aMachine learning.
650 1 4 _aComputer Imaging, Vision, Pattern Recognition and Graphics.
650 2 4 _aComputing Milieux.
650 2 4 _aComputer and Information Systems Applications.
650 2 4 _aMachine Learning.
700 1 _aSheng, Bin.
_eeditor.
_0(orcid)0000-0001-8678-2784
_1https://orcid.org/0000-0001-8678-2784
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aAubreville, Marc.
_eeditor.
_0(orcid)0000-0002-5294-5247
_1https://orcid.org/0000-0002-5294-5247
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031336577
776 0 8 _iPrinted edition:
_z9783031336591
830 0 _aLecture Notes in Computer Science,
_x1611-3349 ;
_v13597
856 4 0 _zLibro electrónico
_uhttp://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-33658-4
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
912 _aZDB-2-LNC
942 _cLIBRO_ELEC
999 _c261660
_d261659