Search Results for author: Simo Saarakkala

Found 18 papers, 11 papers with code

End-To-End Prediction of Knee Osteoarthritis Progression With Multi-Modal Transformers

no code implementations3 Jul 2023 Egor Panfilov, Simo Saarakkala, Miika T. Nieminen, Aleksei Tiulpin

In this study, we leveraged recent advances in Deep Learning and, using a Transformer approach, developed a unified framework for the multi-modal fusion of knee imaging data.

Predicting Knee Osteoarthritis Progression from Structural MRI using Deep Learning

1 code implementation26 Jan 2022 Egor Panfilov, Simo Saarakkala, Miika T. Nieminen, Aleksei Tiulpin

Accurate prediction of knee osteoarthritis (KOA) progression from structural MRI has a potential to enhance disease understanding and support clinical trials.

Machine Learning Based Texture Analysis of Patella from X-Rays for Detecting Patellofemoral Osteoarthritis

no code implementations3 Jun 2021 Neslihan Bayramoglu, Miika T. Nieminen, Simo Saarakkala

Objective is to assess the ability of texture features for detecting radiographic patellofemoral osteoarthritis (PFOA) from knee lateral view radiographs.

BIG-bench Machine Learning Texture Classification

Automated Detection of Patellofemoral Osteoarthritis from Knee Lateral View Radiographs Using Deep Learning: Data from the Multicenter Osteoarthritis Study (MOST)

no code implementations12 Jan 2021 Neslihan Bayramoglu, Miika T. Nieminen, Simo Saarakkala

Patellar region-of-interest (ROI) was first automatically detected, and subsequently, end-to-end deep convolutional neural networks (CNNs) were trained and validated to detect the status of patellofemoral OA.

object-detection Object Detection

A Lightweight CNN and Joint Shape-Joint Space (JS2) Descriptor for Radiological Osteoarthritis Detection

1 code implementation24 May 2020 Neslihan Bayramoglu, Miika T. Nieminen, Simo Saarakkala

Knee osteoarthritis (OA) is very common progressive and degenerative musculoskeletal disease worldwide creates a heavy burden on patients with reduced quality of life and also on society due to financial impact.

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

1 code implementation Preprint on arXiv 2020 Huy Hoang Nguyen, Simo Saarakkala, Matthew Blaschko, Aleksei Tiulpin

Finally, when compared to a well-tuned fully supervised baseline that yielded a balanced accuracy (BA) of $70. 9\pm0. 8%$ on the test set, Semixup had comparable performance -- BA of $71\pm0. 8%$ $(p=0. 368)$ while requiring $6$ times less labeled data.

Adaptive Segmentation of Knee Radiographs for Selecting the Optimal ROI in Texture Analysis

no code implementations21 Aug 2019 Neslihan Bayramoglu, Aleksei Tiulpin, Jukka Hirvasniemi, Miika T. Nieminen, Simo Saarakkala

Compared to the current state-of-the-art approaches, our results suggest that the proposed adaptive ROI approach in texture analysis of subchondral bone can increase the diagnostic performance for detecting the presence of radiographic OA.

Texture Classification

KNEEL: Knee Anatomical Landmark Localization Using Hourglass Networks

4 code implementations29 Jul 2019 Aleksei Tiulpin, Iaroslav Melekhov, Simo Saarakkala

This paper addresses the challenge of localization of anatomical landmarks in knee X-ray images at different stages of osteoarthritis (OA).

Transfer Learning

Deep-Learning for Tidemark Segmentation in Human Osteochondral Tissues Imaged with Micro-computed Tomography

1 code implementation11 Jul 2019 Aleksei Tiulpin, Mikko Finnilä, Petri Lehenkari, Heikki J. Nieminen, Simo Saarakkala

In this paper, we present the first application of Deep Learning to PTA-stained osteochondral samples that allows to perform tidemark segmentation in a fully-automatic manner.

Image Segmentation Segmentation +1

A novel method for automatic localization of joint area on knee plain radiographs

no code implementations31 Jan 2017 Aleksei Tiulpin, Jérôme Thevenot, Esa Rahtu, Simo Saarakkala

The obtained results for the used datasets show the mean intersection over the union equal to: 0. 84, 0. 79 and 0. 78.

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