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SAF-IS: a Spatial Annotation Free Framework for Instance Segmentation of Surgical Tools
In this work, we develop a framework for instance segmentation not relying on spatial annotations for training.
Autonomous Intraluminal Navigation of a Soft Robot using Deep-Learning-based Visual Servoing
Navigation inside luminal organs is an arduous task that requires non-intuitive coordination between the movement of the operator's hand and the information obtained from the endoscopic video.
FUN-SIS: a Fully UNsupervised approach for Surgical Instrument Segmentation
We then use the obtained instrument masks as pseudo-labels in order to train a per-frame segmentation model; to this aim, we develop a learning-from-noisy-labels architecture, designed to extract a clean supervision signal from these pseudo-labels, leveraging their peculiar noise properties.
A Kinematic Bottleneck Approach For Pose Regression of Flexible Surgical Instruments directly from Images
3-D pose estimation of instruments is a crucial step towards automatic scene understanding in robotic minimally invasive surgery.
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