Surgical Video Frame Prediction With Multi-Task Prompt Learning
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Solution Overview
Problem
Computer-assisted surgery systems face challenges with computationally expensive temporal networks that are not suitable for real-time use in generating multi-task frame predictions during surgical procedures.
Innovation Solution
A multi-task prompt learning (MTPL) model is employed, utilizing a spatio-temporal prompting network (STPN) with a cross-task prompt network (CTPN) and prompt refinement decoder head (PRDH) to generate multi-task frame predictions efficiently on low-powered processing systems, enabling real-time multi-task learning in surgical video analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If temporal networks are used for multi-task frame predictions, then prediction accuracy is improved, but computational cost increases making real-time use infeasible
Solution Approach 1:
The patent segments the temporal network into multiple specialized components: a spatio-temporal feature extractor that processes spatial features, and separate temporal feature extractors for each task (phase prediction, tool prediction, anatomy prediction). This segmentation allows each component to be optimized independently, reducing overall computational cost while maintaining prediction accuracy across multiple tasks simultaneously.
Solution Approach 2:
The patent implements a universal spatio-temporal feature extractor that serves all prediction tasks (phase, tool, and anatomy predictions). This multi-functional component processes the video input once and generates spatio-temporal features that are reused across multiple task-specific predictors, eliminating redundant computations and enabling real-time processing while maintaining accurate multi-task predictions.
2Measurement precision
If multiple separate models are used for different prediction tasks, then task-specific accuracy is improved, but system complexity and computational resources increase
Solution Approach 1:
The patent merges multiple prediction tasks into a unified multi-task learning framework. The spatio-temporal feature extractor combines spatial and temporal processing, while task-specific prompts (phase prompts, tool prompts, anatomy prompts) are integrated into a single model architecture. This combining approach maintains task-specific accuracy through specialized prompt modules while reducing overall system complexity by sharing the core feature extraction infrastructure across all tasks.
Solution Approach 2:
The patent introduces task-specific prompts as intermediary components between the shared spatio-temporal feature extractor and the final predictions. These prompts (phase prompts, tool prompts, anatomy prompts) act as mediators that adapt the general spatio-temporal features to specific task requirements, enabling accurate task-specific predictions without requiring separate complete models for each task.
3Productivity
If computational resources are increased to handle temporal networks, then real-time processing is enabled, but hardware requirements and cost increase
Solution Approach 1:
The patent implements dynamic feature extraction where the spatio-temporal feature extractor adaptively processes video inputs based on task requirements. The temporal feature extractors dynamically attend to relevant time steps for each specific task (phase, tool, anatomy prediction), processing only the necessary temporal information rather than uniformly processing all temporal data. This dynamic approach enables real-time processing with reduced computational resources by avoiding redundant calculations.
Data Source
AI summary
Examples described herein provide a computer-implemented method for generating multi-task frame predictions for a current frame of a video of a surgical procedure. The method includes receiving historical video frames. The method further includes generating a plurality of multi-task prompts based on the historical video frames. The method further includes generating a plurality of spatial temporal embeddings based on the historical video frames and a current video frame. The method further includes generating multi-task frame predictions based on the plurality of multi-task prompts and the plurality of spatial temporal embeddings.