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Found 44 papers, 15 papers with code
Efficient Domain Adaptation for Endoscopic Visual Odometry
In this work, an efficient neural style transfer framework for endoscopic visual odometry is proposed, which compresses the time from pre-operative planning to testing phase to less than five minutes.
Implicit Shape Modeling for Anatomical Structure Refinement of Volumetric Medical Images
In this paper, a unified framework is proposed for 3D shape modelling and segmentation refinement based on implicit neural networks.
CDFI: Cross Domain Feature Interaction for Robust Bronchi Lumen Detection
Endobronchial intervention is increasingly used as a minimally invasive means for the treatment of pulmonary diseases.
Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation
In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution.
MR Elastography with Optimization-Based Phase Unwrapping and Traveling Wave Expansion-based Neural Network (TWENN)
Here we propose a pipeline for processing MRE images using optimization-based displacement extraction and Traveling Wave Expansion-based Neural Network (TWENN) modulus estimation.
Revisiting Self-Supervised Contrastive Learning for Facial Expression Recognition
The success of most advanced facial expression recognition works relies heavily on large-scale annotated datasets.
Differentiable Topology-Preserved Distance Transform for Pulmonary Airway Segmentation
Detailed pulmonary airway segmentation is a clinically important task for endobronchial intervention and treatment of peripheral located lung cancer lesions.
A Compacted Structure for Cross-domain learning on Monocular Depth and Flow Estimation
A dual-head mechanism is used to predict optical flow for rigid and non-rigid motion based on a divide-and-conquer manner, which significantly improves the optical flow estimation performance.
Re-thinking and Re-labeling LIDC-IDRI for Robust Pulmonary Cancer Prediction
The LIDC-IDRI database is the most popular benchmark for lung cancer prediction.
Tackling Long-Tailed Category Distribution Under Domain Shifts
Two new datasets were proposed for this problem, named AWA2-LTS and ImageNet-LTS.
A Long Short-term Memory Based Recurrent Neural Network for Interventional MRI Reconstruction
Here we proposed a convolutional long short-term memory (Conv-LSTM) based recurrent neural network (RNN), or ConvLR, to reconstruct interventional images with golden-angle radial sampling.
Faithful learning with sure data for lung nodule diagnosis
In this study, we construct a sure dataset with pathologically-confirmed labels and propose a collaborative learning framework to facilitate sure nodule classification by integrating unsure data knowledge through nodule segmentation and malignancy score regression.
Occlusion-Invariant Rotation-Equivariant Semi-Supervised Depth Based Cross-View Gait Pose Estimation
It can generalize well on the real-world data from all the other unseen views.
TransAction: ICL-SJTU Submission to EPIC-Kitchens Action Anticipation Challenge 2021
In this report, the technical details of our submission to the EPIC-Kitchens Action Anticipation Challenge 2021 are given.
Adversarial Invariant Learning
To the best of our knowledge, this is one of the first to adopt differentiable environment splitting method to enable stable predictions across environments without environment index information, which achieves the state-of-the-art performance on datasets with strong spurious correlation, such as Colored MNIST.
Learning Tubule-Sensitive CNNs for Pulmonary Airway and Artery-Vein Segmentation in CT
Training convolutional neural networks (CNNs) for segmentation of pulmonary airway, artery, and vein is challenging due to sparse supervisory signals caused by the severe class imbalance between tubular targets and background.
Batch Group Normalization
Among previous normalization methods, Batch Normalization (BN) performs well at medium and large batch sizes and is with good generalizability to multiple vision tasks, while its performance degrades significantly at small batch sizes.
Alleviating Class-wise Gradient Imbalance for Pulmonary Airway Segmentation
Due to the small size and scattered spatial distribution of peripheral bronchi, this is hampered by severe class imbalance between foreground and background regions, which makes it challenging for CNN-based methods to parse distal small airways.
End-to-End Real-time Catheter Segmentation with Optical Flow-Guided Warping during Endovascular Intervention
In this paper, we present FW-Net, an end-to-end and real-time deep learning framework for endovascular intervention.
Constrained-Space Optimization and Reinforcement Learning for Complex Tasks
The effectiveness of the proposed method is verified with a robotic suturing task, demonstrating that the learned policy outperformed the experts' demonstrations in terms of the smoothness of the joint motion and end-effector trajectories, as well as the overall task completion time.
DDU-Nets: Distributed Dense Model for 3D MRI Brain Tumor Segmentation
Segmentation of brain tumors and their subregions remains a challenging task due to their weak features and deformable shapes.
Artificial Intelligence in Surgery
Artificial Intelligence (AI) is gradually changing the practice of surgery with the advanced technological development of imaging, navigation and robotic intervention.
Z-Net: an Anisotropic 3D DCNN for Medical CT Volume Segmentation
Accurate volume segmentation from the Computed Tomography (CT) scan is a common prerequisite for pre-operative planning, intra-operative guidance and quantitative assessment of therapeutic outcomes in robot-assisted Minimally Invasive Surgery (MIS).
Instantiation-Net: 3D Mesh Reconstruction from Single 2D Image for Right Ventricle
3D shape instantiation which reconstructs the 3D shape of a target from limited 2D images or projections is an emerging technique for surgical intervention.
A Robust Regression Approach for Robot Model Learning
Machine learning and data analysis have been used in many robotics fields, especially for modelling.
U-Net Training with Instance-Layer Normalization
However, two potential issues exist in BIN: first, the Clip function is not differentiable at input values of 0 and 1; second, the combined feature map is not with a normalized distribution which is harmful for signal propagation in DCNN.
One-stage Shape Instantiation from a Single 2D Image to 3D Point Cloud
An average point cloud-to-point cloud (PC-to-PC) error of 1. 72mm has been achieved, which is comparable to the PLSR-based (1. 42mm) and KPLSR-based (1. 31mm) two-stage shape instantiation algorithm.
AirwayNet: A Voxel-Connectivity Aware Approach for Accurate Airway Segmentation Using Convolutional Neural Networks
Airway segmentation on CT scans is critical for pulmonary disease diagnosis and endobronchial navigation.
Real-time 3D Shape Instantiation for Partially-deployed Stent Segment from a Single 2D Fluoroscopic Image in Robot-assisted Fenestrated Endovascular Aortic Repair
As original GCN is for classification, in this paper, the coarsening layers are removed and the softmax function at the network end is replaced with linear mapping for the regression task.
ACNN: a Full Resolution DCNN for Medical Image Segmentation
These down sampling layers decrease the spatial dimension of feature maps, which can be detrimental to image segmentation.
Varifocal-Net: A Chromosome Classification Approach using Deep Convolutional Networks
To expedite the diagnosis, we present a novel method named Varifocal-Net for simultaneous classification of chromosome's type and polarity using deep convolutional networks.
3D Path Planning from a Single 2D Fluoroscopic Image for Robot Assisted Fenestrated Endovascular Aortic Repair
The current standard of intra-operative navigation during Fenestrated Endovascular Aortic Repair (FEVAR) calls for need of 3D alignments between inserted devices and aortic branches.
Normalization in Training U-Net for 2D Biomedical Semantic Segmentation
In this paper, four normalization methods - BN, IN, LN and GN are compared in details, specifically for 2D biomedical semantic segmentation.
Translational Motion Compensation for Soft Tissue Velocity Images
Methods: Translational motion is estimated from a suitable region of velocities automatically defined in the left-ventricular volume.
Abdominal Aortic Aneurysm Segmentation with a Small Number of Training Subjects
In this paper, the AAA was segmented using U-net with a small number (two) of training subjects.
Artificial Intelligence and Robotics
The recent successes of AI have captured the wildest imagination of both the scientific communities and the general public.
A Real-time and Registration-free Framework for Dynamic Shape Instantiation
Secondly, kernel partial least squares regression (KPLSR) was used to learn the relationship between the pre-operative 3D SSM and a synchronized 2D SSM constructed from 2D images obtained at the approximate optimal scan plane.
Multi-stage Suture Detection for Robot Assisted Anastomosis based on Deep Learning
In robotic surgery, task automation and learning from demonstration combined with human supervision is an emerging trend for many new surgical robot platforms.
Towards Automatic 3D Shape Instantiation for Deployed Stent Grafts: 2D Multiple-class and Class-imbalance Marker Segmentation with Equally-weighted Focal U-Net
Previously, a semi-automatic 3D shape instantiation method was developed to instantiate the 3D shape of a main, deployed, and fenestrated stent graft from a single fluoroscopy projection in real-time, which could help 3D FEVAR navigation and robotic path planning.
Real-time 3D Shape Instantiation from Single Fluoroscopy Projection for Fenestrated Stent Graft Deployment
Currently, this process is guided by 2D fluoroscopy, which is uninformative and error prone.
Self-Supervised Siamese Learning on Stereo Image Pairs for Depth Estimation in Robotic Surgery
Robotic surgery has become a powerful tool for performing minimally invasive procedures, providing advantages in dexterity, precision, and 3D vision, over traditional surgery.
4D Cardiac Ultrasound Standard Plane Location by Spatial-Temporal Correlation
The proposed method is comprised of three stages, the frame smoothing, spatial-temporal embedding and final classification.
Robust Image Descriptors for Real-Time Inter-Examination Retargeting in Gastrointestinal Endoscopy
Despite successes with optical biopsy for in vivo and in situ tissue characterisation, biopsy retargeting for serial examinations is challenging because tissue may change in appearance between examinations.
Real-time 3D Tracking of Articulated Tools for Robotic Surgery
The proposed method is based on the CAD model of the tools as well as robot kinematics to generate online part-based templates for efficient 2D matching and 3D pose estimation.
