Ein Organpriorisiertes Y-Net-Multimodales Bildsegmentierungsmodell unter Verwendung eines Gemeinsamen Modalitäten-Encoders und von Decodern

Details

Veröffentlichungs-Nr.

EP4811284

Anmeldetag

22. Dezember 2025

Veröffentlichung

23. September 2026

Rechtsraum

EP

IPC

G06T7/11

Abstract

Conventional techniques for multi-modal image segmentation are not computationally effective for image analysis. Embodiments herein provide a method and system for an organ prioritized Y-Net multi-modal image segmentation model using a joint modality encoder and decoders. The method trains a genetic Y-Net multi-modal image segmentation model comprising the joint modality encoder, a bottleneck, and a plurality of modality-specific decoders, with each of a plurality of the multi-modal medical images, for a plurality of predefined hyperparameters, to generate a trained Y-Net multi-modal image segmentation model. Further a multi-objective organ prioritization reward function with an organ prioritization is formulated for the plurality of predefined hyperparameters of the trained Y-Net multi-modal image segmentation model. Further the plurality of parameters of the trained Y-Net multi-modal image segmentation model is optimized using the multi-objective organ prioritization reward function, to generate the organ prioritized Y-Net multi-modal image segmentation model.

Anmelder

Firma

Tata Consultancy Services Limited

Land

🇮🇳 Indien

Fachgebiete