Search Results for author:
Found 8 papers, 5 papers with code
MOSformer: Momentum encoder-based inter-slice fusion transformer for medical image segmentation
2. 5D-based segmentation models bridge computational efficiency of 2D-based models and spatial perception capabilities of 3D-based models.
MICRO: Model-Based Offline Reinforcement Learning with a Conservative Bellman Operator
This method trades off performance and robustness via introducing the robust Bellman operator into the algorithm.
CROP: Conservative Reward for Model-based Offline Policy Optimization
Offline reinforcement learning (RL) aims to optimize policy using collected data without online interactions.
DOMAIN: MilDly COnservative Model-BAsed OfflINe Reinforcement Learning
However, uncertainty estimation is unreliable and leads to poor performance in certain scenarios, and the previous methods ignore differences between the model data, which brings great conservatism.
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.
