1 citations · 1 across the 6 of their papers we have counts for
6 papers
H2: A Dual Hybrid Semantic Data Lake Architecture for Medical Data Harmonization with Human-In-the-Loop verified, LLM Driven Metadata Annotation System
Ioannis N. Tzortzis, Georgia Kapetadimitri, Agapi Davradou +6
Medical data, by its nature, exhibit a high degree of heterogeneity on multiple levels ranging from (a) different modalities like images, text and time series, (b) diverse tabular…
Learning using privileged information for segmenting tumors on digital mammograms
Ioannis N. Tzortzis, Konstantinos Makantasis, Ioannis Rallis +3
Limited amount of data and data sharing restrictions, due to GDPR compliance, constitute two common factors leading to reduced availability and accessibility when referring to medi…
A Few-Shot Attention Recurrent Residual U-Net for Crack Segmentation
Iason Katsamenis, Eftychios Protopapadakis, Nikolaos Bakalos +3
Recent studies indicate that deep learning plays a crucial role in the automated visual inspection of road infrastructures. However, current learning schemes are static, implying n…
Transferability limitations for Covid 3D Localization Using SARS-CoV-2 segmentation models in 4D CT images
Constantine Maganaris, Eftychios Protopapadakis, Nikolaos Bakalos +3
In this paper, we investigate the transferability limitations when using deep learning models, for semantic segmentation of pneumonia-infected areas in CT images. The proposed appr…
Evaluating Transferability for Covid 3D Localization Using CT SARS-CoV-2 segmentation models
Constantine Maganaris, Eftychios Protopapadakis, Nikolaos Bakalos +3
Recent studies indicate that detecting radiographic patterns on CT scans can yield high sensitivity and specificity for Covid-19 localization. In this paper, we investigate the app…
Space-Time Domain Tensor Neural Networks: An Application on Human Pose Classification
Konstantinos Makantasis, Athanasios Voulodimos, Anastasios Doulamis +2
Recent advances in sensing technologies require the design and development of pattern recognition models capable of processing spatiotemporal data efficiently. In this study, we pr…