[MedIA] Accompanying paper list and source code for survey "A comprehensive survey on deep active learning in medical image analysis"
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Updated
May 22, 2024 - Python
[MedIA] Accompanying paper list and source code for survey "A comprehensive survey on deep active learning in medical image analysis"
This GitHub repository hosts the notebooks and tools developed as part of this thesis to automate the extraction, processing, and analysis of data from the MICCAI 2023 conference, aiding in the systematic review and providing a structured foundation for further research in this crucial area.
MARIO Challenge MICCAI 2024
Contribution to the ToothFairy Challenge (MICCAI 2023).
Code for "Pérez-García et al. 2021, Transfer Learning of Deep Spatiotemporal Networks to Model Arbitrarily Long Videos of Seizures, MICCAI 2021".
Contribution to the SEG.A Challenge (MICCAI 2023) by Marek Wodzinski
MICCAI2023: Artifact Restoration in Histology Images with Diffusion Probabilistic Models
[MICCAI2023] Pytorch implementation for 'Regressing Simulation to Real: Unsupervised Domain Adaptation for Automated Quality Assessment in Transoesophageal Echocardiography'
[STACOM-MICCAI 2019] Deep Learning Registration for Cardiac Motion Tracking
MICCAI 2023: Learning Deep Intensity Field for Extremely Sparse-View CBCT Reconstruction
MICCAI 2022: Calibrating Label Distribution for Class-Imbalanced Barely-Supervised Knee Segmentation
The souce code of MICCAI'23 paper: Combat Long-tails in Medical Classification with Relation-aware Consistency and Virtual Features Compensation
MICCAI Ophthalmic Medical Image Analysis 2022 publication code
AIOZ AI - Overcoming Data Limitation in Medical Visual Question Answering (MICCAI 2019)
MedGIFT contribution to the BONBID challenge (MICCAI 2023)
[MICCAI 2021] Boundary-aware Transformers for Skin Lesion Segmentation
Contribution to the MVSEG Challenge (MICCAI 2023)
[MICCAI 2022 Best Paper Finalist] Bayesian Pseudo Labels: Expectation Maximization for Robust and Efficient Semi Supervised Segmentation
[MICCAI 2022] Official Implementation for "Hybrid Spatio-Temporal Transformer Network for Predicting Ischemic Stroke Lesion Outcomes from 4D CT Perfusion Imaging"
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