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Found 9 papers, 0 papers with code
PlutoNet: An Efficient Polyp Segmentation Network with Modified Partial Decoder and Decoder Consistency Training
With PlutoNet, we propose a novel \emph{decoder consistency training} approach that consists of a shared encoder, the modified partial decoder which is a combination of the partial decoder and full-scale connections that capture salient features at different scales without being redundant, and the auxiliary decoder which focuses on higher-level relevant semantic features.
Human Gaze Guided Attention for Surgical Activity Recognition
Accurately recognizing surgical activities in video poses a challenging problem that requires an effective means of learning both spatial and temporal dynamics.
An Efficient Polyp Segmentation Network
Our proposed model requires fewer parameters as well as outperforms the state-of-the-art models.
MIcro-Surgical Anastomose Workflow recognition challenge report
The best models achieved more than 95% AD-Accuracy for phase recognition, 80% for step recognition, 60% for activity recognition, and 75% for all granularity levels.
Surgical Data Science -- from Concepts toward Clinical Translation
We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process.
Detection and Localization of Robotic Tools in Robot-Assisted Surgery Videos Using Deep Neural Networks for Region Proposal and Detection
We propose an architecture using multimodal convolutional neural networks for fast detection and localization of tools in RAS videos.
Towards Generalizable Surgical Activity Recognition Using Spatial Temporal Graph Convolutional Networks
The proposed modality is based on spatial temporal graph representations of surgical tools in videos, for surgical activity recognition.
Surgical Gesture Recognition with Optical Flow only
While Simonyan uses both RGB frames and dense optical flow, we use only dense optical flow representations as input to emphasize the role of motion in surgical gesture recognition, and present it as a robust alternative to kinematic data.
Joint Surgical Gesture and Task Classification with Multi-Task and Multimodal Learning
Our experimental results show that our approach is superior compared to an ar- chitecture that classifies the gestures and surgical tasks separately on visual cues and motion cues respectively.
