Simo Saarakkala
Orcid: 0000-0003-2850-5484
According to our database1,
Simo Saarakkala
authored at least 24 papers
between 2014 and 2024.
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Bibliography
2024
Clinically-Inspired Multi-Agent Transformers for Disease Trajectory Forecasting From Multimodal Data.
IEEE Trans. Medical Imaging, January, 2024
Inter- and Intra-Day Precision of a Low-Cost and Wearable Bioelectrical Impedance Analysis Device.
Proceedings of the Digital Health and Wireless Solutions - First Nordic Conference, 2024
2023
End-To-End Prediction of Knee Osteoarthritis Progression With Multi-Modal Transformers.
CoRR, 2023
Deep Learning for Predicting Progression of Patellofemoral Osteoarthritis Based on Lateral Knee Radiographs, Demographic Data and Symptomatic Assessments.
CoRR, 2023
A Stronger Baseline For Automatic Pfirrmann Grading Of Lumbar Spine Mri Using Deep Learning.
Proceedings of the 20th IEEE International Symposium on Biomedical Imaging, 2023
2022
Machine learning based texture analysis of patella from X-rays for detecting patellofemoral osteoarthritis.
Int. J. Medical Informatics, 2022
Proceedings of the 19th IEEE International Symposium on Biomedical Imaging, 2022
CLIMAT: Clinically-Inspired Multi-Agent Transformers for Knee Osteoarthritis Trajectory Forecasting.
Proceedings of the 19th IEEE International Symposium on Biomedical Imaging, 2022
2021
Automated Detection of Patellofemoral Osteoarthritis from Knee Lateral View Radiographs Using Deep Learning: Data from the Multicenter Osteoarthritis Study (MOST).
CoRR, 2021
2020
Semixup: In- and Out-of-Manifold Regularization for Deep Semi-Supervised Knee Osteoarthritis Severity Grading From Plain Radiographs.
IEEE Trans. Medical Imaging, 2020
A Lightweight CNN and Joint Shape-Joint Space (JS2) Descriptor for Radiological Osteoarthritis Detection.
CoRR, 2020
A Lightweight CNN and Joint Shape-Joint Space (JS<sup>2</sup>) Descriptor for Radiological Osteoarthritis Detection.
Proceedings of the Medical Image Understanding and Analysis - 24th Annual Conference, 2020
Deep-Learning for Tidemark Segmentation in Human Osteochondral Tissues Imaged with Micro-computed Tomography.
Proceedings of the Advanced Concepts for Intelligent Vision Systems, 2020
2019
An Automatic Regularization Method: An Application for 3-D X-Ray Micro-CT Reconstruction Using Sparse Data.
IEEE Trans. Medical Imaging, 2019
Adaptive Segmentation of Knee Radiographs for Selecting the Optimal ROI in Texture Analysis.
CoRR, 2019
Automatic Grading of Individual Knee Osteoarthritis Features in Plain Radiographs using Deep Convolutional Neural Networks.
CoRR, 2019
Multimodal Machine Learning-based Knee Osteoarthritis Progression Prediction from Plain Radiographs and Clinical Data.
CoRR, 2019
Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision Workshops, 2019
Improving Robustness of Deep Learning Based Knee MRI Segmentation: Mixup and Adversarial Domain Adaptation.
Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision Workshops, 2019
2017
Automatic Knee Osteoarthritis Diagnosis from Plain Radiographs: A Deep Learning-Based Approach.
CoRR, 2017
Proceedings of the Image Analysis - 20th Scandinavian Conference, 2017
Automatic Segmentation of Bone Tissue from Computed Tomography Using a Volumetric Local Binary Patterns Based Method.
Proceedings of the Image Analysis - 20th Scandinavian Conference, 2017
2014
Local Binary Patterns to Evaluate Trabecular Bone Structure from Micro-CT Data: Application to Studies of Human Osteoarthritis.
Proceedings of the Computer Vision - ECCV 2014 Workshops, 2014