Learning Where to Look While Tracking Instruments in Robot-assisted Surgery
This work addresses the challenge of directing task-specific attention in surgical interventions, offering incremental improvements for robotic surgery applications.
The paper tackles the problem of tracking surgical instruments in robot-assisted surgery by proposing an end-to-end multitask learning model for real-time segmentation and attention prediction, which outperforms state-of-the-art models on most evaluation metrics using the MICCAI dataset.
Directing of the task-specific attention while tracking instrument in surgery holds great potential in robot-assisted intervention. For this purpose, we propose an end-to-end trainable multitask learning (MTL) model for real-time surgical instrument segmentation and attention prediction. Our model is designed with a weight-shared encoder and two task-oriented decoders and optimized for the joint tasks. We introduce batch-Wasserstein (bW) loss and construct a soft attention module to refine the distinctive visual region for efficient saliency learning. For multitask optimization, it is always challenging to obtain convergence of both tasks in the same epoch. We deal with this problem by adopting `poly' loss weight and two phases of training. We further propose a novel way to generate task-aware saliency map and scanpath of the instruments on MICCAI robotic instrument segmentation dataset. Compared to the state of the art segmentation and saliency models, our model outperforms most of the evaluation metrics.