Semi-Supervised Bladder Tissue Classification in Multi-Domain Endoscopic Images.

Date publication

octobre 2023

Journal

IEEE transactions on bio-medical engineering

Auteurs

Membres identifiés du Cancéropôle Est :
Pr DE MATHELIN Michel

Tous les auteurs :
Lazo JF, Rosa B, Catellani M, Fontana M, Mistretta FA, Musi G, de Cobelli O, de Mathelin M, De Momi E

Lien Pubmed

https://www.ncbi.nlm.nih.gov/pubmed/37037233

Résumé

Accurate visual classification of bladder tissue during Trans-Urethral Resection of Bladder Tumor (TURBT) procedures is essential to improve early cancer diagnosis and treatment. During TURBT interventions, White Light Imaging (WLI) and Narrow Band Imaging (NBI) techniques are used for lesion detection. Each imaging technique provides diverse visual information that allows clinicians to identify and classify cancerous lesions. Computer vision methods that use both imaging techniques could improve endoscopic diagnosis. We address the challenge of tissue classification when annotations are available only in one domain, in our case WLI, and the endoscopic images correspond to an unpaired dataset, i.e. there is no exact equivalent for every image in both NBI and WLI domains.

Référence

IEEE Trans Biomed Eng. 2023 10;70(10):2822-2833