activity
20222026
most citedFuzzy Pooling

34 citations · 47 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CV2026

Multi-Resolution Feature Fusion U-Net for Magnetic Resonance Imaging Segmentation

Eirini Cholopoulou, Dimitrios E. Diamantis, Dimitris K. Iakovidis

The segmentation of anatomical structures in medical images and particularly in MRI scans, is essential for clinical diagnosis and monitoring disease progression. While Deep Learni…

cs.CV2025

Multiscale Vector-Quantized Variational Autoencoder for Endoscopic Image Synthesis

Dimitrios E. Diamantis, Dimitris K. Iakovidis

Gastrointestinal (GI) imaging via Wireless Capsule Endoscopy (WCE) generates a large number of images requiring manual screening. Deep learning-based Clinical Decision Support (CDS…

cs.CV2024

Clinical Evaluation of Medical Image Synthesis: A Case Study in Wireless Capsule Endoscopy

Panagiota Gatoula, Dimitrios E. Diamantis, Anastasios Koulaouzidis +10

Synthetic Data Generation (SDG) based on Artificial Intelligence (AI) can transform the way clinical medicine is delivered by overcoming privacy barriers that currently render clin…

cs.CV2024

Soft-Hard Attention U-Net Model and Benchmark Dataset for Multiscale Image Shadow Removal

Eirini Cholopoulou, Dimitrios E. Diamantis, Dimitra-Christina C. Koutsiou +1

Effective shadow removal is pivotal in enhancing the visual quality of images in various applications, ranging from computer vision to digital photography. During the last decades…

cs.CV2023★ 13 cited

This Intestine Does Not Exist: Multiscale Residual Variational Autoencoder for Realistic Wireless Capsule Endoscopy Image Generation

Dimitrios E. Diamantis, Panagiota Gatoula, Anastasios Koulaouzidis +1

Medical image synthesis has emerged as a promising solution to address the limited availability of annotated medical data needed for training machine learning algorithms in the con…

cs.LG2022★ 34 cited

Fuzzy Pooling

Dimitrios E. Diamantis, Dimitris K. Iakovidis

Convolutional Neural Networks (CNNs) are artificial learning systems typically based on two operations: convolution, which implements feature extraction through filtering, and pool…