Stop the war!Остановите войну!
for scientists:
default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDMIDOG/DRAC@MICCAI@MICCAI 2022: Singapore
- Bin Sheng, Marc Aubreville
:
Mitosis Domain Generalization and Diabetic Retinopathy Analysis - MICCAI Challenges MIDOG 2022 and DRAC 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18-22, 2022, Proceedings. Lecture Notes in Computer Science 13597, Springer 2023, ISBN 978-3-031-33657-7
DRAC
- Felix Krause, Dominik Heindl, Hana Jebril, Markus Karner, Markus Unterdechler:
nnU-Net Pre- and Postprocessing Strategies for UW-OCTA Segmentation Tasks in Diabetic Retinopathy Analysis. 5-15 - Linus Kreitner, Ivan Ezhov, Daniel Rueckert, Johannes C. Paetzold, Martin J. Menten:
Automated Analysis of Diabetic Retinopathy Using Vessel Segmentation Maps as Inductive Bias. 16-25 - Renyu Li, Yunchao Gu, Xinliang Wang, Sixu Lu:
Bag of Tricks for Diabetic Retinopathy Grading of Ultra-Wide Optical Coherence Tomography Angiography Images. 26-30 - Zhenyu Chen, Liqin Huang:
Deep Convolutional Neural Network for Image Quality Assessment and Diabetic Retinopathy Grading. 31-37 - Zhiqiang Gao, Jinquan Guo:
Diabetic Retinal Overlap Lesion Segmentation Network. 38-45 - Yuhan Zheng, Fuping Wu, Bartlomiej W. Papiez:
An Ensemble Method to Automatically Grade Diabetic Retinopathy with Optical Coherence Tomography Angiography Images. 46-58 - Gitaek Kwon, Eunjin Kim, Sunho Kim, Seongwon Bak, Minsung Kim, Jaeyoung Kim:
Bag of Tricks for Developing Diabetic Retinopathy Analysis Framework to Overcome Data Scarcity. 59-73 - Junlin Hou, Fan Xiao, Jilan Xu, Yuejie Zhang, Haidong Zou, Rui Feng:
Deep-OCTA: Ensemble Deep Learning Approaches for Diabetic Retinopathy Analysis on OCTA Images. 74-87 - Jungrae Cho, Byungeun Shon, Sungmoon Jeong:
Deep Learning-Based Multi-tasking System for Diabetic Retinopathy in UW-OCTA Images. 88-96 - Zhuoyi Tan, Hizmawati Madzin, Zeyu Ding:
Semi-supervised Semantic Segmentation Methods for UW-OCTA Diabetic Retinopathy Grade Assessment. 97-117 - Zhuoyi Tan, Hizmawati Madzin, Zeyu Ding:
Image Quality Assessment Based on Multi-model Ensemble Class-Imbalance Repair Algorithm for Diabetic Retinopathy UW-OCTA Images. 118-126 - Xin Chen, Yanbin Chen, Chaonan Lin, Lin Pan:
An Improved U-Net for Diabetic Retinopathy Segmentation. 127-134 - Sungjin Choi, Bosoung Jeoun, Jaeyoung Anh, Jae-hyup Jeong, Yongjin Choi, Dowan Kwon, Unho Kim, Seoyoung Shin:
A Vision Transformer Based Deep Learning Architecture for Automatic Diagnosis of Diabetic Retinopathy in Optical Coherence Tomography Angiography. 135-145 - Yihao Li, Rachid Zeghlache, Ikram Brahim, Hui Xu, Yubo Tan, Pierre-Henri Conze, Mathieu Lamard, Gwenolé Quellec, Mostafa El Habib Daho:
Segmentation, Classification, and Quality Assessment of UW-OCTA Images for the Diagnosis of Diabetic Retinopathy. 146-160 - Zhicheng Wu, Yanbin Chen, Xuru Zhang, Liqin Huang:
Data Augmentation by Fourier Transformation for Class-Imbalance: Application to Medical Image Quality Assessment. 161-169 - Wen Zhang, Hao Chen, Daisong Li, Shaohua Zheng:
Automatic Image Quality Assessment and DR Grading Method Based on Convolutional Neural Network. 170-177 - Xiaochao Yan, Zhaopei Li, Jianhui Wen, Lin Pan:
A Transfer Learning Based Model Ensemble Method for Image Quality Assessment and Diabetic Retinopathy Grading. 178-185 - Farhana Sultana, Abu Sufian, Paramartha Dutta:
Automatic Diabetic Retinopathy Lesion Segmentation in UW-OCTA Images Using Transfer Learning. 186-194
MIDOG
- Jonas Ammeling, Frauke Wilm, Jonathan Ganz, Katharina Breininger, Marc Aubreville:
Reference Algorithms for the Mitosis Domain Generalization (MIDOG) 2022 Challenge. 201-205 - Jonas Annuscheit, Christian Krumnow:
Radial Prediction Domain Adaption Classifier for the MIDOG 2022 Challenge. 206-210 - Hongyan Gu, Mohammad Haeri, Shuo Ni, Christopher Kazu Williams, Neda Zarrin-Khameh, Shino Magaki, Xiang 'Anthony' Chen:
Detecting Mitoses with a Convolutional Neural Network for MIDOG 2022 Challenge. 211-216 - Satoshi Kondo, Satoshi Kasai, Kousuke Hirasawa:
Tackling Mitosis Domain Generalization in Histopathology Images with Color Normalization. 217-220 - Sujatha Kotte, VG Saipradeep, Naveen Sivadasan, Thomas Joseph, Hrishikesh Sharma, Vidushi Walia, Binuja Varma, Geetashree Mukherjee:
A Deep Learning Based Ensemble Model for Generalized Mitosis Detection in H &E Stained Whole Slide Images. 221-225 - Maxime W. Lafarge, Viktor H. Koelzer:
Fine-Grained Hard-Negative Mining: Generalizing Mitosis Detection with a Fifth of the MIDOG 2022 Dataset. 226-233 - Ziyue Wang, Yang Chen, Zijie Fang, Hao Bian, Yongbing Zhang:
Multi-task RetinaNet for Mitosis Detection. 234-240
a service of 
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
last updated on 2024-07-02 21:45 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
