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Found 6 papers, 3 papers with code
2020 CATARACTS Semantic Segmentation Challenge
In 2020, we released pixel-wise semantic annotations for anatomy and instruments for 4670 images sampled from 25 videos of the CATARACTS training set.
Robust Medical Instrument Segmentation Challenge 2019
The validation of the competing methods for the three tasks (binary segmentation, multi-instance detection and multi-instance segmentation) was performed in three different stages with an increasing domain gap between the training and the test data.
BARNet: Bilinear Attention Network with Adaptive Receptive Fields for Surgical Instrument Segmentation
For the scale variation, our adaptive receptive field module aggregates multi-scale features and automatically fuses them with different weights.
Attention-Guided Lightweight Network for Real-Time Segmentation of Robotic Surgical Instruments
LWANet adopts encoder-decoder architecture, where the encoder is the lightweight network MobileNetV2, and the decoder consists of depthwise separable convolution, attention fusion block, and transposed convolution.
RAUNet: Residual Attention U-Net for Semantic Segmentation of Cataract Surgical Instruments
To the best of our knowledge, this is the first cataract surgical instrument dataset for semantic segmentation.
RASNet: Segmentation for Tracking Surgical Instruments in Surgical Videos Using Refined Attention Segmentation Network
The dataset from the MICCAI EndoVis Challenge 2017 is used to evaluate our network.
