Recurr-NET: A multimodal pre-operative image-based deep-learning model for predicting hepatocellular carcinoma outcomes
M: Medicine – Surgery – Orthopaedics – Material for disabled
Informations
- Stand number
- B79
- Exhibition class
- M: Medicine – Surgery – Orthopaedics – Material for disabled
- Technical description
- Multimodal deep-learning model which 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.
- Simplified description
- We have created a computer model that uses images and medical information to predict outcomes after liver cancer surgery. It is more accurate than the traditional methods used to estimate these outcomes.
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
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