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ORCIDPRIME@MICCAI 2021: Strasbourg, France
- Islem Rekik
, Ehsan Adeli, Sang Hyun Park, Julia A. Schnabel:
Predictive Intelligence in Medicine - 4th International Workshop, PRIME 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings. Lecture Notes in Computer Science 12928, Springer 2021, ISBN 978-3-030-87601-2 - Dong Kyu Won, Euijin Jung, Sion An, Philip Chikontwe, Sang Hyun Park:
Low-Dose CT Denoising Using Pseudo-CT Image Pairs. 1-10 - Alaa Bessadok, Ahmed Nebli, Mohamed Ali Mahjoub, Gang Li, Weili Lin, Dinggang Shen, Islem Rekik:
A Few-Shot Learning Graph Multi-trajectory Evolution Network for Forecasting Multimodal Baby Connectivity Development from a Baseline Timepoint. 11-24 - Umut Guvercin, Mohammed Amine Gharsallaoui, Islem Rekik:
One Representative-Shot Learning Using a Population-Driven Template with Application to Brain Connectivity Classification and Evolution Prediction. 25-36 - Myeongkyun Kang, Philip Chikontwe, Miguel Luna, Kyung Soo Hong, June Hong Ahn, Sang Hyun Park:
Mixing-AdaSIN: Constructing a De-biased Dataset Using Adaptive Structural Instance Normalization and Texture Mixing. 37-46 - Bolin Lai, Yuhsuan Wu, Xiaoyu Bai, Xiaoyun Zhou, Peng Wang, Jinzheng Cai, Yuankai Huo, Lingyun Huang, Yong Xia, Jing Xiao, Le Lu, Heping Hu, Adam P. Harrison:
Liver Tumor Localization and Characterization from Multi-phase MR Volumes Using Key-Slice Prediction: A Physician-Inspired Approach. 47-58 - Yunbi Liu, Genggeng Qin, Yun Liu, Mingxia Liu, Wei Yang:
Improving Tuberculosis Recognition on Bone-Suppressed Chest X-Rays Guided by Task-Specific Features. 59-69 - Furkan Pala, Islem Mhiri, Islem Rekik:
Template-Based Inter-modality Super-Resolution of Brain Connectivity. 70-82 - Anastasia Butskova, Rain Juhl, Dzenan Zukic, Aashish Chaudhary, Kilian M. Pohl, Qingyu Zhao:
Adversarial Bayesian Optimization for Quantifying Motion Artifact Within MRI. 83-92 - Xiaping Du, Jiayu Huo, Yuanfang Qiao, Qian Wang, Lichi Zhang:
False Positive Suppression in Cervical Cell Screening via Attention-Guided Semi-supervised Learning. 93-103 - Mohammed Amine Gharsallaoui, Furkan Tornaci, Islem Rekik:
Investigating and Quantifying the Reproducibility of Graph Neural Networks in Predictive Medicine. 104-116 - Shaked Perek, Mika Amit, Efrat Hexter:
Self Supervised Contrastive Learning on Multiple Breast Modalities Boosts Classification Performance. 117-127 - Zhuochen Wang, Tingting Huang, Bin Xiao, Jiayu Huo, Sheng Wang, Haoxiang Jiang, Heng Liu, Fan Wu, Xiang Sean Zhou, Zhong Xue, Jian Yang, Qian Wang:
Self-guided Multi-attention Network for Periventricular Leukomalacia Recognition. 128-137 - Fakai Wang, Kang Zheng, Yirui Wang, Xiaoyun Zhou, Le Lu, Jing Xiao, Min Wu, Chang-Fu Kuo, Shun Miao:
Opportunistic Screening of Osteoporosis Using Plain Film Chest X-Ray. 138-146 - Vincent Andrearczyk, Pierre Fontaine, Valentin Oreiller, Joël Castelli, Mario Jreige, John O. Prior, Adrien Depeursinge:
Multi-task Deep Segmentation and Radiomics for Automatic Prognosis in Head and Neck Cancer. 147-156 - Dongren Yao, Erkun Yang, Li Sun, Jing Sui, Mingxia Liu:
Integrating Multimodal MRIs for Adult ADHD Identification with Heterogeneous Graph Attention Convolutional Network. 157-167 - Muhammad Febrian Rachmadi, Maria del C. Valdés Hernández, Rizal Maulana, Joanna M. Wardlaw, Stephen D. Makin, Henrik Skibbe:
Probabilistic Deep Learning with Adversarial Training and Volume Interval Estimation - Better Ways to Perform and Evaluate Predictive Models for White Matter Hyperintensities Evolution. 168-180 - Chanho Kim, Won Hwa Kim, Hye Jung Kim, Jaeil Kim:
A Multi-scale Capsule Network for Improving Diagnostic Generalizability in Breast Cancer Diagnosis Using Ultrasonography. 181-191 - Ho Kyung Shin, Won Hwa Kim, Hye Jung Kim, Chanho Kim, Jaeil Kim:
Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy Using Multi-scale Patch Learning with Mammography. 192-200 - Vasileios Baltatzis, Kyriaki-Margarita Bintsi, Loïc Le Folgoc, Octavio E. Martinez Manzanera, Sam Ellis, Arjun Nair, Sujal Desai, Ben Glocker, Julia A. Schnabel:
The Pitfalls of Sample Selection: A Case Study on Lung Nodule Classification. 201-211 - Haoyi Tao, Yuanfang Qiao, Lichi Zhang, Yiqiang Zhan, Zhong Xue, Qian Wang:
Anatomical Structure-Aware Pulmonary Nodule Detection via Parallel Multi-task RoI Head. 212-220 - Sebastian Cygert, Franciszek Górski, Piotr Juszczyk, Sebastian Lewalski, Krzysztof Pastuszak, Andrzej Czyzewski, Anna Supernat:
Towards Cancer Patients Classification Using Liquid Biopsy. 221-230 - Aviral Chharia, Neeraj Kumar:
Foreseeing Survival Through 'Fuzzy Intelligence': A Cognitively-Inspired Incremental Learning Based de novo Model for Breast Cancer Prognosis by Multi-Omics Data Fusion. 231-242 - Lara Dular, Ziga Spiclin:
Improving Across Dataset Brain Age Predictions Using Transfer Learning. 243-254 - Ufuk Demir, Atahan Ozer, Yusuf Huseyin Sahin, Gozde Unal:
Uncertainty-Based Dynamic Graph Neighborhoods for Medical Segmentation. 255-265 - Guris Özen, Ahmed Nebli, Islem Rekik:
FLAT-Net: Longitudinal Brain Graph Evolution Prediction from a Few Training Representative Templates. 266-278
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