RobSurv uses vector quantization to learn noise-resistant discrete features alongside continuous features, improving CT/PET survival prediction and noise robustness on three cancer datasets.
Synergynet: Bridging the gap be- tween discrete and continuous representations for pre- cise medical image segmentation
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RobSurv: Vector Quantization-Based Multi-Modal Learning for Robust Cancer Survival Prediction
RobSurv uses vector quantization to learn noise-resistant discrete features alongside continuous features, improving CT/PET survival prediction and noise robustness on three cancer datasets.