{"id":1449,"date":"2025-09-08T18:42:06","date_gmt":"2025-09-08T15:42:06","guid":{"rendered":"https:\/\/bmi.med.duth.gr\/?page_id=1449"},"modified":"2026-03-31T10:34:24","modified_gmt":"2026-03-31T07:34:24","slug":"theses","status":"publish","type":"page","link":"https:\/\/bmi.med.duth.gr\/index.php\/en\/theses\/","title":{"rendered":"MSc Theses"},"content":{"rendered":"\n<div class=\"wp-block-group is-layout-constrained wp-block-group-is-layout-constrained\">\n<p style=\"font-size:clamp(14px, 0.875rem + ((1vw - 3.2px) * 0.313), 18px);\"><strong>Theses text are also available from the MSc Zenodo Community: <a href=\"https:\/\/zenodo.org\/communities\/biomedinf \">https:\/\/zenodo.org\/communities\/biomedinf <\/a><br><br>Theses code can be forked via the MSc Github: <a href=\"https:\/\/github.com\/MSc-Biomedical-Informatics-DUTH-ATHENA\">https:\/\/github.com\/MSc-Biomedical-Informatics-DUTH-ATHENA<\/a><\/strong><\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p style=\"font-size:clamp(14px, 0.875rem + ((1vw - 3.2px) * 0.313), 18px);\"><\/p>\n<\/div>\n\n\n\n<p style=\"font-size:clamp(14px, 0.875rem + ((1vw - 3.2px) * 0.313), 18px);\"><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li style=\"font-size:clamp(14px, 0.875rem + ((1vw - 3.2px) * 0.313), 18px);\">Tzatzimaki A., <strong>Multi-modal Data Collection to Inform Artificial Intelligence based Clinical Physiotherapy<\/strong>, MSc Thesis, Master of Science in Biomedical Informatics, School of Medicine, Democritus University of Thrace, and ATHENA Research Center, Greece, March, 2026. <a href=\"https:\/\/doi.org\/10.5281\/zenodo.19290578\">https:\/\/doi.org\/10.5281\/zenodo.19290578<\/a> [<a href=\"https:\/\/bmi.med.duth.gr\/wp-content\/uploads\/2026\/03\/MSc_Thesis_TzatzimakiA_March2026.pdf\">Thesis<\/a> &#8211; <a href=\"https:\/\/bmi.med.duth.gr\/wp-content\/uploads\/2026\/03\/MSc_Thesis_TzatzimakiA_March2026_Presentation.pdf\">Presentation<\/a>]&nbsp;<\/li>\n<\/ul>\n\n\n\n<p style=\"font-size:clamp(14px, 0.875rem + ((1vw - 3.2px) * 0.313), 18px);\"><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li style=\"font-size:clamp(14px, 0.875rem + ((1vw - 3.2px) * 0.313), 18px);\">Kalitsi G, <strong>Digital Biomarkers of Stress and Anxiety: Assessing the Quality and Diversity of Wearable Sensor Datasets<\/strong>, MSc Thesis, Master of Science in Biomedical Informatics, School of Medicine, Democritus University of Thrace, and ATHENA Research Center, Alexandroupoli, Greece, March 2026. <a href=\"https:\/\/doi.org\/10.5281\/zenodo.19006535\">https:\/\/doi.org\/10.5281\/zenodo.19006535<\/a> [<a href=\"https:\/\/bmi.med.duth.gr\/wp-content\/uploads\/2026\/03\/BMI_Kalitsi_March2026-1.pdf\">Thesis<\/a>]&nbsp;<\/li>\n<\/ul>\n\n\n\n<p style=\"font-size:clamp(14px, 0.875rem + ((1vw - 3.2px) * 0.313), 18px);\"><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li style=\"font-size:clamp(14px, 0.875rem + ((1vw - 3.2px) * 0.313), 18px);\">Kourtidis G, <strong>Physical Activity Data Acquisition and Processing via Inertial Sensor Networks<\/strong>, MSc Thesis, MSc in Biomedical Informatics, Democritus University of Thrace and ATHENA Research Center, Greece, December 2025 [in Greek] <a href=\"https:\/\/doi.org\/10.5281\/zenodo.17404654\">https:\/\/doi.org\/10.5281\/zenodo.17404654<\/a> <a href=\"https:\/\/doi.org\/10.5281\/zenodo.17404653\"><strong><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-palette-color-4-color\">[<\/mark><\/strong><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-palette-color-4-color\">Thesis<\/mark><strong><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-palette-color-4-color\">]<\/mark><\/strong><\/a>&nbsp;<\/li>\n<\/ul>\n\n\n\n<p style=\"font-size:clamp(14px, 0.875rem + ((1vw - 3.2px) * 0.313), 18px);\"><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li style=\"font-size:clamp(14px, 0.875rem + ((1vw - 3.2px) * 0.313), 18px);\">Pechlivanis D, <strong>Anonymization and curation of diagnostic ultrasound data for artificial intelligence applications<\/strong>, MSc Thesis, MSc in Biomedical Informatics, Democritus University of Thrace and ATHENA Research Center, Greece, June 2025 [in Greek] <a href=\"https:\/\/doi.org\/10.5281\/zenodo.15463124\">https:\/\/doi.org\/10.5281\/zenodo.15463124<\/a> <a href=\"https:\/\/doi.org\/10.5281\/zenodo.15463124\"><strong><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-palette-color-4-color\">[<\/mark><\/strong><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-palette-color-4-color\">Thesis<\/mark><strong><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-palette-color-4-color\">]<\/mark><\/strong><\/a>&nbsp;<\/li>\n<\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li style=\"font-size:clamp(14px, 0.875rem + ((1vw - 3.2px) * 0.313), 18px);\">Didaskalou M, <strong>Deep Learning Approaches for Thrombosis Detection and Risk Assessment via Ultrasound Imaging<\/strong>, MSc Thesis, MSc in Biomedical Informatics, Democritus University of Thrace and ATHENA Research Center, Greece, March 2025 <a href=\"https:\/\/doi.org\/10.5281\/zenodo.14941449\">https:\/\/doi.org\/10.5281\/zenodo.14941449<\/a>&nbsp; <a href=\"https:\/\/zenodo.org\/records\/14941449\"><strong><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-palette-color-4-color\">[<\/mark><\/strong><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-palette-color-4-color\">Thesis<\/mark><strong><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-palette-color-4-color\">]<\/mark><\/strong><\/a><\/li>\n<\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li style=\"font-size:clamp(14px, 0.875rem + ((1vw - 3.2px) * 0.313), 18px);\">Arnidis I,&nbsp;<strong>Artificial Intelligence Techniques for Detecting Blood Vessels in Ultrasound Images<\/strong>,&nbsp;MSc Thesis, MSc in Biomedical Informatics, Democritus University of Thrace and ATHENA Research Center, Greece, January 2025 [in Greek] <a href=\"https:\/\/doi.org\/10.5281\/zenodo.14767371\">https:\/\/doi.org\/10.5281\/zenodo.14767371<\/a> <a href=\"https:\/\/zenodo.org\/records\/14767371\"><strong><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-palette-color-4-color\">[<\/mark><\/strong><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-palette-color-4-color\">Thesis<\/mark><strong><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-palette-color-4-color\">]<\/mark><\/strong><\/a><\/li>\n<\/ul>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Theses text are also available from the MSc Zenodo Community: https:\/\/zenodo.org\/communities\/biomedinf Theses code can be forked via the MSc Github: https:\/\/github.com\/MSc-Biomedical-Informatics-DUTH-ATHENA<\/p>\n","protected":false},"author":18,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_gspb_post_css":"","footnotes":""},"class_list":["post-1449","page","type-page","status-publish","hentry"],"blocksy_meta":[],"_links":{"self":[{"href":"https:\/\/bmi.med.duth.gr\/index.php\/wp-json\/wp\/v2\/pages\/1449","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/bmi.med.duth.gr\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/bmi.med.duth.gr\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/bmi.med.duth.gr\/index.php\/wp-json\/wp\/v2\/users\/18"}],"replies":[{"embeddable":true,"href":"https:\/\/bmi.med.duth.gr\/index.php\/wp-json\/wp\/v2\/comments?post=1449"}],"version-history":[{"count":13,"href":"https:\/\/bmi.med.duth.gr\/index.php\/wp-json\/wp\/v2\/pages\/1449\/revisions"}],"predecessor-version":[{"id":1639,"href":"https:\/\/bmi.med.duth.gr\/index.php\/wp-json\/wp\/v2\/pages\/1449\/revisions\/1639"}],"wp:attachment":[{"href":"https:\/\/bmi.med.duth.gr\/index.php\/wp-json\/wp\/v2\/media?parent=1449"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}