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

01.03.2019

Clinical decision-support systems are often built by manually gathering, formalising and implementing specialist knowledge. Therefore, they are limited by existing human knowledge concerning modelling clinical conditions, diagnosis and therapy, and can be inaccurate because of variations and complexity inherent to medical data. In order to circumvent these limitations, following an obvious growth of publicly available collections of medical-radiology diagnostic images, machine learning has become an irreplaceable tool for solving various problems concerning radiology image analysis.

The goal of this project is to utilise the strength of contemporary findings in the machine-learning and computer-vision field for building quality models for: 1) automating the process for radius bone fracture detection and localisation from arm radiogram, and; 2) automating the process for early diagnosis of arthritis from multimodal (hyperspectral, thermographic, 3D) scans of patient’s hand. This will be achieved through understanding the process of clinical interpretation in medical diagnostics, developing matching physical models of mentioned phenomena for feature extraction and generating synthetic data, whilst using large collections of labelled data for learning models for tissue localisation/segmentation and detection/classification of said phenomena.

The project is conducted at RITEH.

Duration:
2019–2022
Contributors:
Ivan Štajduhar, Jonatan Lerga, Franko Hržić, Teo Manojlović, Matija Milanič, Dražen Brščić, Damir Miletić
Funded by:
University of Rijeka
  • University of Rijeka

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Latest Projects

Machine Learning for Knowledge Transfer in Medical Radiology

Estimating River Discharges in Highly Stratified Estuaries

Multilayer Framework for the Information Spreading Characterization in Social Media during the COVID-19 Crisis (InfoCoV)

European Network for assuring food integrity using non-destructive spectral sensors

National Competence Centres in the Framework of EuroHPC (EUROCC)

Latest Research Papers

Rule-Based EEG Classifier Utilizing Local Entropy of Time–Frequency Distributions

Rethinking Effects of Innovation in Competition In The Era of New Digital Technologies

A Comparison of Approaches for Measuring the Semantic Similarity of Short Texts Based on Word Embeddings

Indoor Localization Based on Infrared Angle of Arrival Sensor Network

Gravitational-Wave Burst Signals Denoising Based on the Adaptive Modification of Intersection of Confidence Intervals Rule

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U Rijeci radi Centar za umjetnu inteligenciju, već su u prvoj godini rada povukli šest milijuna u 14 projekata

UNIRI Excellence Awards in Science

Talk on conference “Exploring Digital Legal Landscapes”

ICAIH 2020 conference presentation

International conference “Exploring Digital Legal Landscapes” – 11th of December, 2020

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Center for Artificial Intelligence and Cybersecurity
  • jlerga@airi.uniri.hr
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