Oulu-IMEDS/semixup

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Python
Created over 4 years ago
Updated 8 months ago

Oulu-IMEDS/semixup

Oulu-IMEDS

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Semixup: In- and Out-of-Manifold Regularization for Deep Semi-Supervised Knee Osteoarthritis Severity Grading from Plain Radiographs

solt

Streaming over lightweight data transformations

Jupyter Notebook

pytorch_bn_fusion

Batch normalization fusion for PyTorch

OAProgression

Multimodal Machine Learning-based Knee Osteoarthritis Progression Prediction from Plain Radiographs and Clinical Data

DeepKnee

Codes for paper: Automatic Knee Osteoarthritis Diagnosis from Plain Radiographs: A Deep Learning-Based Approach

KNEEL

Hourglass Networks for Knee Anatomical Landmark Localization: PyTorch Implementation

stambo

Statistical model comparison with bootstrap an beyond

Jupyter Notebook

KneeOARSIGrading

Grading individual knee osteoarthritis features using Deep Learning

AdaTriplet

greedy_ensembles_training

Greedy Bayesian Posterior Approximation with Deep Ensembles. A. Tiulpin and M. B. Blaschko. (2021)

CLIMATv2

CLIMATv2: Clinically-Inspired Multi-Agent Transformers for Disease Trajectory Forecasting from Multimodal Data

KneeLocalizer

Codes for paper: A novel method for automatic localization of joint area on knee plain radiographs by A. Tiulpin et. al.

DeepWrist

Deep Learning Pipeline for Wrist Fracture Detection

CLIMAT

CLIMAT: Clinically-Inspired Multi-Agent Transformers for Disease Trajectory Forecasting from Multi-modal Data

OAProgressionMR

Prediction of knee osteoarthritis progression from DESS MRI