Sparse multi-label classification of medical images using deep convolutional neural networks

The recent advancements in imaging technologies have improved clinician's ability to detect, diagnose, and treat diseases. As an example, radiologists routinely interpret medical images and summarize their findings in the form of radiology reports. The mapping of visual information present in m...

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Bibliographic Details
Main Authors:	Linardos, Euangelos, Λινάρδος, Ευάγγελος
Other Authors:	Kavallieratou, Ergina
Language:	English
Published:	2021
Subjects:	machine learning deep learning image processing computer vision medical imaging convolutional neural networks multi labels sparse labels imageCLEFmed concept detection keras tensorflow μηχανική μάθηση βαθιά μάθηση επεξεργασία εικόνας υπολογιστική όραση ιατρική απεικόνιση συνελικτικά νευρωνικά δίκτυα πολλαπλές ετικέτες αραιές ετικέτες ανίχνευση έννοιας Machine learning Artificial intelligence Diagnostic imaging Image processing Computer vision Neural networks (Computer science)
Online Access:	http://hdl.handle.net/11610/21660
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Sparse multi-label classification of medical images using deep convolutional neural networks

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