Laboratory for Artificial Intelligence in Medicine and Biotechnology

Laboratory for Artificial Intelligence in Medicine and Biotechnology focuses on delivering human-centred data-driven solutions for aiding the modern decision-making process in health care.

Laboratory for Artificial Intelligence in Medicine and Biotechnology

Machine Learning for Knowledge Transfer in Medical Radiology

Development of machine-learning-based techniques for illness and injury detection in medical images

Image Processing, Information Engineering & Interdisciplinary Knowledge Exchange

Fracture Recognition in Paediatric Wrist Radiographs: An Object Detection Approach

Rapid extraction of skin physiological parameters from hyperspectral images using machine learning

Modeling Uncertainty in Fracture Age Estimation from Pediatric Wrist Radiographs

Cast suppression in radiographs by generative adversarial networks

Deep Semi-Supervised Algorithm for Learning Cluster-Oriented Representations of Medical Images Using Partially Observable DICOM Tags and Images

XAOM: A method for automatic alignment and orientation of radiographs for computer-aided medical diagnosis

Automatic Annotation of Narrative Radiology Reports

Adaptive Filtering and Analysis of EEG Signals in the Time-Frequency Domain Based on the Local Entropy

Local-Entropy Based Approach for X-Ray Image Segmentation and Fracture Detection

An Adaptive Method Based on the Improved LPA-ICI Algorithm for MRI Enhancement

Mirroring Quasi-Symmetric Organ Observations for Reducing Problem Complexity

Semi-automated detection of anterior cruciate ligament injury from MRI

Algorithm Based On the Short-Term Rényi Entropy And IF Estimation For Noisy EEG Signals Analysis

Uncensoring censored data for machine learning: A likelihood-based approach

Learning Bayesian networks from survival data using weighting censored instances

Impact of censoring on learning Bayesian networks in survival modelling

University of Rijeka

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University of Rijeka

About the Center

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