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ORCID5th MLMIR@MICCAI 2022: Singapore
- Nandinee Fariah Haq
, Patricia Johnson, Andreas Maier, Chen Qin, Tobias Würfl, Jaejun Yoo:
Machine Learning for Medical Image Reconstruction - 5th International Workshop, MLMIR 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings. Lecture Notes in Computer Science 13587, Springer 2022, ISBN 978-3-031-17246-5
Deep Learning for Magnetic Resonance Imaging
- Weijian Huang, Cheng Li, Wenxin Fan, Ziyao Zhang, Tong Zhang, Yongjin Zhou, Qiegen Liu, Shanshan Wang:
Rethinking the Optimization Process for Self-supervised Model-Driven MRI Reconstruction. 3-13 - Samah Khawaled, Moti Freiman:
NPB-REC: Non-parametric Assessment of Uncertainty in Deep-Learning-Based MRI Reconstruction from Undersampled Data. 14-23 - Jan Nikolas Morshuis, Sergios Gatidis, Matthias Hein, Christian F. Baumgartner:
Adversarial Robustness of MR Image Reconstruction Under Realistic Perturbations. 24-33 - Jingshuai Liu, Chen Qin, Mehrdad Yaghoobi:
High-Fidelity MRI Reconstruction with the Densely Connected Network Cascade and Feature Residual Data Consistency Priors. 34-43 - Jaa-Yeon Lee, Min A. Yoon, Choong Guen Chee, Jae Hwan Cho, Jin Hoon Park, Sung-Hong Park:
Metal Artifact Correction MRI Using Multi-contrast Deep Neural Networks for Diagnosis of Degenerative Spinal Diseases. 44-52 - Mert Acar, Tolga Çukur, Ilkay Öksüz:
Segmentation-Aware MRI Reconstruction. 53-61 - Yilmaz Korkmaz, Muzaffer Özbey, Tolga Çukur:
MRI Reconstruction with Conditional Adversarial Transformers. 62-71
Deep Learning for General Image Reconstruction
- Ye Li, Jianan Cui, Junyu Chen, Guodong Zeng, Scott Wollenweber, Floris Jansen, Se-In Jang, Kyung Sang Kim, Kuang Gong, Quanzheng Li:
A Noise-Level-Aware Framework for PET Image Denoising. 75-83 - Ce Wang, Kun Shang, Haimiao Zhang, Qian Li, S. Kevin Zhou:
DuDoTrans: Dual-Domain Transformer for Sparse-View CT Reconstruction. 84-94 - Wonjin Kim, Wonkyeong Lee, Sun-Young Jeon, Nayeon Kang, Geonhui Jo, Jang-Hwan Choi:
Deep Denoising Network for X-Ray Fluoroscopic Image Sequences of Moving Objects. 95-104 - Baris Askin, Alper Güngör, Damla Alptekin Soydan, Emine Ulku Saritas, Can Baris Top, Tolga Çukur:
PP-MPI: A Deep Plug-and-Play Prior for Magnetic Particle Imaging Reconstruction. 105-114 - Mayank Katare, Mahesh Raveendranatha Panicker, A. N. Madhavanunni, Gayathri Malamal:
Learning While Acquisition: Towards Active Learning Framework for Beamforming in Ultrasound Imaging. 115-122 - Temitope Emmanuel Komolafe, Yuhang Sun, Nizhuan Wang, Kaicong Sun, Guohua Cao, Dinggang Shen:
DPDudoNet: Deep-Prior Based Dual-Domain Network for Low-Dose Computed Tomography Reconstruction. 123-132 - Sunggu Kyung, JongJun Won, Seongyong Pak, Gil-Sun Hong, Namkug Kim:
MTD-GAN: Multi-task Discriminator Based Generative Adversarial Networks for Low-Dose CT Denoising. 133-144 - Viswanath P. Sudarshan, K. Pavan Kumar Reddy, Mohana Singh, Jayavardhana Gubbi, Arpan Pal:
Uncertainty-Informed Bayesian PET Image Reconstruction Using a Deep Image Prior. 145-155
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