MV-MS-FETE: Multi-View Multi-Scale Feature Extractor and Transformer Encoder for Stenosis Recognition in Echocardiograms

Abstract

Aortic stenosis is a common heart valve disease that mainly affects older people in developed countries. Its early detection is crucial to prevent the irreversible disease progression and, eventually, death. A typical screening technique to detect stenosis uses echocardiograms; however, variations introduced by other tissues, camera movements, and uneven lighting can hamper the visual inspection, leading to misdiagnosis. To address these issues, effective solutions involve employing deep learning algorithms to assist clinicians in detecting and classifying stenosis by developing models that can predict this pathology from single heart views. Although promising, the visual information conveyed by a single image may not be sufficient for an accurate diagnosis, especially when using an automatic system; thus, this indicates that different solutions should be explored.

Publication
Computer Methods and Programs in Biomedicine
Irene Cannistraci
Irene Cannistraci
Ph.D. Student in Computer Science, Sapienza University of Rome
GLADIA Research Group

Visiting Researcher Student,
Helmholtz Munich
AIDOS Lab

I am a Ph.D. student in Computer Science passionate about Deep Learning.