The invention
Recurr-NET: A multimodal pre-operative image-based deep-learning model for predicting hepatocellular carcinoma outcomes
The invention
- Name of invention
- Recurr-NET: A multimodal pre-operative image-based deep-learning model for predicting hepatocellular carcinoma outcomes
- Recurr-NET : Un modèle d'apprentissage profond multimodal basé sur l'image préopératoire pour prédire les résultats du carcinome hépatocellulaire
Invention description
- Description
- Our multimodal deep-learning model integrates imaging and clinical data for predicting primary liver cancer (hepatocellular carcinoma) outcomes after surgery. It has superior performance to established scores and histology markers for prognostication
INVENTORS
Philip Leung-Ho YU inventor 3727293023_3323
Man Fung YUEN inventor 3727293023_3322
Jianliang LU inventor 3727293023_3321
Ho-Ming CHENG inventor 3727293023_3320
Keith Wan-Hang CHIU inventor 3727293023_3319
Rex Wan-Hin HUI inventor 3727293023_3318
Information in this online catalogue is based on data received for the Event and updated by the participant. Catalogue entries remain the sole responsability of the participant. The Exhibition management declines all responsibility for any possible errors or omissions.