LEADER 05724nam 22007095 450 001 9910728397403321 005 20230529092907.0 010 $a3-031-33658-5 024 7 $a10.1007/978-3-031-33658-4 035 $a(MiAaPQ)EBC30554471 035 $a(Au-PeEL)EBL30554471 035 $a(DE-He213)978-3-031-33658-4 035 $a(BIP)091206233 035 $a(PPN)270612440 035 $a(CKB)26801494500041 035 $a(EXLCZ)9926801494500041 100 $a20230529d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMitosis Domain Generalization and Diabetic Retinopathy Analysis $eMICCAI Challenges MIDOG 2022 and DRAC 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18?22, 2022, Proceedings /$fedited by Bin Sheng, Marc Aubreville 205 $a1st ed. 2023. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2023. 215 $a1 online resource (250 pages) 225 1 $aLecture Notes in Computer Science,$x1611-3349 ;$v13597 311 08$aPrint version: Sheng, Bin Mitosis Domain Generalization and Diabetic Retinopathy Analysis Cham : Springer International Publishing AG,c2023 9783031336577 327 $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. . 330 $aThis book constitutes two challenges that were held in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, which took place in Singapore during September 18-22, 2022. The peer-reviewed 20 long and 5 short papers included in this volume stem from the following three biomedical image analysis challenges: Mitosis Domain Generalization Challenge (MIDOG 2022), Diabetic Retinopathy Analysis Challenge (CRAC 2022) The challenges share the need for developing and fairly evaluating algorithms that increase accuracy, reproducibility and efficiency of automated image analysis in clinically relevant applications. 410 0$aLecture Notes in Computer Science,$x1611-3349 ;$v13597 606 $aImage processing?Digital techniques 606 $aComputer vision 606 $aComputers 606 $aApplication software 606 $aMachine learning 606 $aComputer Imaging, Vision, Pattern Recognition and Graphics 606 $aComputing Milieux 606 $aComputer and Information Systems Applications 606 $aMachine Learning 610 $aOphthalmology 610 $aMedical 615 0$aImage processing?Digital techniques. 615 0$aComputer vision. 615 0$aComputers. 615 0$aApplication software. 615 0$aMachine learning. 615 14$aComputer Imaging, Vision, Pattern Recognition and Graphics. 615 24$aComputing Milieux. 615 24$aComputer and Information Systems Applications. 615 24$aMachine Learning. 676 $a006 676 $a617.735 700 $aSheng$b Bin$01358931 701 $aAubreville$b Marc$01358932 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910728397403321 996 $aMitosis Domain Generalization and Diabetic Retinopathy Analysis$93371744 997 $aUNINA