Reproducibility analysis of automated deep learning based localisation of mandibular canals on a temporal CBCT dataset

no code implementations • 28 Apr 2023 • Jorma Järnstedt, Jaakko Sahlsten, Joel Jaskari, Kimmo Kaski, Helena Mehtonen, Ari Hietanen, Osku Sundqvist, Vesa Varjonen, Vesa Mattila, Sangsom Prapayasotok, Sakarat Nalampang

The RM of the Likert scoring was 0. 923 for the radiologist and 0. 877 for the DLS.

Comparison of Deep Learning Segmentation and Multigrader-annotated Mandibular Canals of Multicenter CBCT scans

no code implementations • 27 May 2022 • Jorma Järnstedt, Jaakko Sahlsten, Joel Jaskari, Kimmo Kaski, Helena Mehtonen, Ziyuan Lin, Ari Hietanen, Osku Sundqvist, Vesa Varjonen, Vesa Mattila, Sangsom Prapayasotok, Sakarat Nalampang

Deep learning approach has been demonstrated to automatically segment the bilateral mandibular canals from CBCT scans, yet systematic studies of its clinical and technical validation are scarce.

Out-of-Distribution Generalization

Uncertainty-aware deep learning methods for robust diabetic retinopathy classification

no code implementations • 22 Jan 2022 • Joel Jaskari, Jaakko Sahlsten, Theodoros Damoulas, Jeremias Knoblauch, Simo Särkkä, Leo Kärkkäinen, Kustaa Hietala, Kimmo Kaski

Automatic classification of diabetic retinopathy from retinal images has been widely studied using deep neural networks with impressive results.

Binary Classification Classification

Deep Learning Fundus Image Analysis for Diabetic Retinopathy and Macular Edema Grading

no code implementations • 16 Apr 2019 • Jaakko Sahlsten, Joel Jaskari, Jyri Kivinen, Lauri Turunen, Esa Jaanio, Kustaa Hietala, Kimmo Kaski

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 which are today used for manual disease screening.