activity
20192026
most citedUncertainty-aware deep learning methods for robust diabetic retinopathy classification

5 citations · 6 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CL2026

Automating Clinical Information Retrieval from Finnish Electronic Health Records Using Large Language Models

Mikko Saukkoriipi, Nicole Hernandez, Jaakko Sahlsten +2

Clinicians often need to retrieve patient-specific information from electronic health records (EHRs), a task that is time-consuming and error-prone. We present a locally deployable…

cs.SI2025

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…

eess.IV2024

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…

physics.med-ph2023

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…

cs.LG2022★ 1 cited

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…

cs.CV2022★ 5 cited

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…

eess.IV2019

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…