5 citations · 10 across the 7 of their papers we have counts for
7 papers
Temporal social network modeling of mobile connectivity data with graph neural networks
Joel Jaskari, Chandreyee Roy, Fumiko Ogushi +3
Graph neural networks (GNNs) have emerged as a state-of-the-art data-driven tool for modeling connectivity data of graph-structured complex networks and integrating information of…
Interactive 3D Segmentation for Primary Gross Tumor Volume in Oropharyngeal Cancer
Mikko Saukkoriipi, Jaakko Sahlsten, Joel Jaskari +12
The main treatment modality for oropharyngeal cancer (OPC) is radiotherapy, where accurate segmentation of the primary gross tumor volume (GTVp) is essential. However, accurate GTV…
Reproducibility analysis of automated deep learning based localisation of mandibular canals on a temporal CBCT dataset
Jorma Järnstedt, Jaakko Sahlsten, Joel Jaskari +8
Preoperative radiological identification of mandibular canals is essential for maxillofacial surgery. This study demonstrates the reproducibility of a deep learning system (DLS) by…
Comparison of Deep Learning Segmentation and Multigrader-annotated Mandibular Canals of Multicenter CBCT scans
Jorma Järnstedt, Jaakko Sahlsten, Joel Jaskari +9
Deep learning approach has been demonstrated to automatically segment the bilateral mandibular canals from CBCT scans, yet systematic studies of its clinical and technical validati…
Uncertainty-aware deep learning methods for robust diabetic retinopathy classification
Joel Jaskari, Jaakko Sahlsten, Theodoros Damoulas +5
Automatic classification of diabetic retinopathy from retinal images has been widely studied using deep neural networks with impressive results. However, there is a clinical need f…
Deep Learning Fundus Image Analysis for Diabetic Retinopathy and Macular Edema Grading
Jaakko Sahlsten, Joel Jaskari, Jyri Kivinen +4
Diabetes is a globally prevalent disease that can cause visible microvascular complications such as diabetic retinopathy and macular edema in the human eye retina, the images of wh…
A Novel Variational Autoencoder with Applications to Generative Modelling, Classification, and Ordinal Regression
Joel Jaskari, Jyri J. Kivinen
We develop a novel probabilistic generative model based on the variational autoencoder approach. Notable aspects of our architecture are: a novel way of specifying the latent varia…