Abstract is missing.
- nnU-Net Pre- and Postprocessing Strategies for UW-OCTA Segmentation Tasks in Diabetic Retinopathy AnalysisFelix Krause, Dominik Heindl, Hana Jebril, Markus Karner, Markus Unterdechler. 5-15 [doi]
- Automated Analysis of Diabetic Retinopathy Using Vessel Segmentation Maps as Inductive BiasLinus Kreitner, Ivan Ezhov, Daniel Rueckert, Johannes C. Paetzold, Martin J. Menten. 16-25 [doi]
- Bag of Tricks for Diabetic Retinopathy Grading of Ultra-Wide Optical Coherence Tomography Angiography ImagesRenyu Li, Yunchao Gu, Xinliang Wang, Sixu Lu. 26-30 [doi]
- Deep Convolutional Neural Network for Image Quality Assessment and Diabetic Retinopathy GradingZhenyu Chen, Liqin Huang. 31-37 [doi]
- Diabetic Retinal Overlap Lesion Segmentation NetworkZhiqiang Gao, Jinquan Guo. 38-45 [doi]
- An Ensemble Method to Automatically Grade Diabetic Retinopathy with Optical Coherence Tomography Angiography ImagesYuhan Zheng, Fuping Wu, Bartlomiej W. Papiez. 46-58 [doi]
- Bag of Tricks for Developing Diabetic Retinopathy Analysis Framework to Overcome Data ScarcityGitaek Kwon, Eunjin Kim, Sunho Kim, Seongwon Bak, MinSung Kim, Jaeyoung Kim. 59-73 [doi]
- Deep-OCTA: Ensemble Deep Learning Approaches for Diabetic Retinopathy Analysis on OCTA ImagesJunlin Hou, Fan Xiao, Jilan Xu, Yuejie Zhang, Haidong Zou, Rui Feng. 74-87 [doi]
- Deep Learning-Based Multi-tasking System for Diabetic Retinopathy in UW-OCTA ImagesJungrae Cho, Byungeun Shon, Sungmoon Jeong. 88-96 [doi]
- Semi-supervised Semantic Segmentation Methods for UW-OCTA Diabetic Retinopathy Grade AssessmentZhuoyi Tan, Hizmawati Madzin, Zeyu Ding 0006. 97-117 [doi]
- Image Quality Assessment Based on Multi-model Ensemble Class-Imbalance Repair Algorithm for Diabetic Retinopathy UW-OCTA ImagesZhuoyi Tan, Hizmawati Madzin, Zeyu Ding 0006. 118-126 [doi]
- An Improved U-Net for Diabetic Retinopathy SegmentationXin Chen, Yanbin Chen, Chaonan Lin, Lin Pan. 127-134 [doi]
- A Vision Transformer Based Deep Learning Architecture for Automatic Diagnosis of Diabetic Retinopathy in Optical Coherence Tomography AngiographySungJin Choi, Bosoung Jeoun, Jaeyoung Anh, Jae-hyup Jeong, Yongjin Choi, Dowan Kwon, Unho Kim, Seoyoung Shin. 135-145 [doi]
- Segmentation, Classification, and Quality Assessment of UW-OCTA Images for the Diagnosis of Diabetic RetinopathyYihao Li, Rachid Zeghlache, Ikram Brahim, Hui Xu, Yubo Tan, Pierre-Henri Conze, Mathieu Lamard, Gwenolé Quellec, Mostafa El Habib Daho. 146-160 [doi]
- Data Augmentation by Fourier Transformation for Class-Imbalance: Application to Medical Image Quality AssessmentZhicheng Wu, Yanbin Chen, Xuru Zhang, Liqin Huang. 161-169 [doi]
- Automatic Image Quality Assessment and DR Grading Method Based on Convolutional Neural NetworkWen Zhang, Hao Chen, Daisong Li, Shaohua Zheng. 170-177 [doi]
- A Transfer Learning Based Model Ensemble Method for Image Quality Assessment and Diabetic Retinopathy GradingXiaochao Yan, Zhaopei Li, Jianhui Wen, Lin Pan. 178-185 [doi]
- Automatic Diabetic Retinopathy Lesion Segmentation in UW-OCTA Images Using Transfer LearningFarhana Sultana, Abu Sufian, Paramartha Dutta. 186-194 [doi]
- Reference Algorithms for the Mitosis Domain Generalization (MIDOG) 2022 ChallengeJonas Ammeling, Frauke Wilm, Jonathan Ganz, Katharina Breininger, Marc Aubreville. 201-205 [doi]
- Radial Prediction Domain Adaption Classifier for the MIDOG 2022 ChallengeJonas Annuscheit, Christian Krumnow. 206-210 [doi]
- Detecting Mitoses with a Convolutional Neural Network for MIDOG 2022 ChallengeHongyan Gu, Mohammad Haeri, Shuo Ni, Christopher Kazu Williams, Neda Zarrin-Khameh, Shino Magaki, Xiang 'Anthony' Chen. 211-216 [doi]
- Tackling Mitosis Domain Generalization in Histopathology Images with Color NormalizationSatoshi Kondo, Satoshi Kasai, Kousuke Hirasawa. 217-220 [doi]
- A Deep Learning Based Ensemble Model for Generalized Mitosis Detection in H &E Stained Whole Slide ImagesSujatha Kotte, VG Saipradeep, Naveen Sivadasan, Thomas Joseph, Hrishikesh Sharma, Vidushi Walia, Binuja Varma, Geetashree Mukherjee. 221-225 [doi]
- Fine-Grained Hard-Negative Mining: Generalizing Mitosis Detection with a Fifth of the MIDOG 2022 DatasetMaxime W. Lafarge, Viktor H. Koelzer. 226-233 [doi]
- Multi-task RetinaNet for Mitosis DetectionZiyue Wang, Yang Chen, Zijie Fang, Hao Bian, Yongbing Zhang. 234-240 [doi]