Temporally Consistent Label Interpolation for Robust Surgical Multi-Task Learning under Challenging Conditions

2026-06-25 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, medium

Summary

The Flow-guided Annotation for Robust Operating Scenes (FAROS) framework addresses the annotation granularity mismatch in surgical multi-task learning. Existing methods struggle because temporal tasks like phase recognition require dense frame-level supervision, while spatial tasks such as instrument segmentation only have sparse keyframe annotations due to high labeling costs. FAROS combines zero-shot segmentation-based mask propagation with optical flow estimation to generate temporally consistent dense pseudo labels from sparse keyframe annotations, overcoming challenges like occlusion and motion blur. These densified labels are integrated into a unified Transformer-based multi-task framework, enabling balanced optimization for surgical phase recognition, step recognition, anticipation, instrument segmentation, and action recognition. Validated on DAVIS 2017, GraSP, MISAW, and AutoLaparo benchmarks, FAROS significantly improves cross-task representation learning and holistic surgical scene understanding.

Key takeaway

For Machine Learning Engineers developing surgical AI, the FAROS framework offers a solution to the common challenge of sparse annotations. By generating dense, temporally consistent pseudo labels through flow-guided interpolation, you can achieve balanced optimization across diverse spatio-temporal tasks like instrument segmentation and phase recognition. This approach significantly improves holistic scene understanding, enabling more robust and accurate computer-assisted interventions even under challenging surgical conditions. Consider integrating similar label densification techniques to enhance your multi-task models.

Key insights

FAROS interpolates sparse surgical annotations into dense, temporally consistent labels using optical flow, balancing multi-task learning.

Principles

• Annotation mismatch impedes multi-task learning.
• Optical flow enhances label propagation.
• Dense pseudo-labels balance task supervision.

Method

FAROS combines zero-shot segmentation mask propagation with optical flow to generate dense, temporally consistent pseudo labels from sparse keyframe annotations. These labels balance supervision in a Transformer-based multi-task framework.

In practice

• Improve surgical phase recognition.
• Enhance instrument segmentation.
• Robust surgical action recognition.

Topics

• Surgical Multi-task Learning
• Label Interpolation
• Optical Flow
• Transformer Models
• Surgical Scene Understanding
• Annotation Granularity

Best for: Computer Vision Engineer, AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.