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Found 32 papers, 15 papers with code
Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation
A medical image adapted SAM, which we have dubbed Medical SAM Adapter (MSA), shows superior performance on 19 medical image segmentation tasks with various image modalities including CT, MRI, ultrasound image, fundus image, and dermoscopic images.
Adaptive Negative Evidential Deep Learning for Open-set Semi-supervised Learning
Open-set semi-supervised learning (Open-set SSL) considers a more practical scenario, where unlabeled data and test data contain new categories (outliers) not observed in labeled data (inliers).
SurgT challenge: Benchmark of Soft-Tissue Trackers for Robotic Surgery
There were two purposes for the creation of this challenge: (1) the establishment of the first standardised benchmark for the research community to assess soft-tissue trackers; and (2) to encourage the development of unsupervised deep learning methods, given the lack of annotated data in surgery.
Biomedical image analysis competitions: The state of current participation practice
Of these, 84% were based on standard architectures.
Objective Surgical Skills Assessment and Tool Localization: Results from the MICCAI 2021 SimSurgSkill Challenge
Using this publicly available dataset and results as a springboard, future work may enable more efficient training of surgeons with advances in surgical data science.
Why Deep Surgical Models Fail?: Revisiting Surgical Action Triplet Recognition through the Lens of Robustness
This is the question that we address in this work.
Pseudo-label Guided Cross-video Pixel Contrast for Robotic Surgical Scene Segmentation with Limited Annotations
Concretely, for trusty representation learning, we propose to incorporate pseudo labels to instruct the pair selection, obtaining more reliable representation pairs for pixel contrast.
Personalizing Federated Medical Image Segmentation via Local Calibration
Personalized FL tackles this issue by only utilizing partial model parameters shared from global server, while keeping the rest to adapt to its own data distribution in the local training of each site.
Exploring Intra- and Inter-Video Relation for Surgical Semantic Scene Segmentation
Automatic surgical scene segmentation is fundamental for facilitating cognitive intelligence in the modern operating theatre.
TraSeTR: Track-to-Segment Transformer with Contrastive Query for Instance-level Instrument Segmentation in Robotic Surgery
Specifically, we introduce the prior query that encoded with previous temporal knowledge, to transfer tracking signals to current instances via identity matching.
Real-time landmark detection for precise endoscopic submucosal dissection via shape-aware relation network
Compared with existing solutions, which either neglect geometric relationships among targeting objects or capture the relationships by using complicated aggregation schemes, the proposed network is capable of achieving satisfactory accuracy while maintaining real-time performance by taking full advantage of the spatial relations among landmarks.
Comparative Validation of Machine Learning Algorithms for Surgical Workflow and Skill Analysis with the HeiChole Benchmark
PURPOSE: Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems.
Ranked #1 on
Surgical phase recognition
on HeiChole Benchmark
Modelling Neighbor Relation in Joint Space-Time Graph for Video Correspondence Learning
This paper presents a self-supervised method for learning reliable visual correspondence from unlabeled videos.
Efficient Global-Local Memory for Real-time Instrument Segmentation of Robotic Surgical Video
On the other hand, we develop an active global memory to gather the global semantic correlation in long temporal range to current one, in which we gather the most informative frames derived from model uncertainty and frame similarity.
Source-Free Domain Adaptive Fundus Image Segmentation with Denoised Pseudo-Labeling
We present a novel denoised pseudo-labeling method for this problem, which effectively makes use of the source model and unlabeled target data to promote model self-adaptation from pseudo labels.
Accurate Grid Keypoint Learning for Efficient Video Prediction
Video prediction methods generally consume substantial computing resources in training and deployment, among which keypoint-based approaches show promising improvement in efficiency by simplifying dense image prediction to light keypoint prediction.
Ranked #1 on
Video Prediction
on KTH
Temporal Memory Relation Network for Workflow Recognition from Surgical Video
In this paper, we propose a novel end-to-end temporal memory relation network (TMRNet) for relating long-range and multi-scale temporal patterns to augment the present features.
One to Many: Adaptive Instrument Segmentation via Meta Learning and Dynamic Online Adaptation in Robotic Surgical Video
To greatly increase the label efficiency, we explore a new problem, i. e., adaptive instrument segmentation, which is to effectively adapt one source model to new robotic surgical videos from multiple target domains, only given the annotated instruments in the first frame.
Future Frame Prediction for Robot-assisted Surgery
Predicting future frames for robotic surgical video is an interesting, important yet extremely challenging problem, given that the operative tasks may have complex dynamics.
Trans-SVNet: Accurate Phase Recognition from Surgical Videos via Hybrid Embedding Aggregation Transformer
In this paper, we introduce, for the first time in surgical workflow analysis, Transformer to reconsider the ignored complementary effects of spatial and temporal features for accurate surgical phase recognition.
Domain Adaptive Robotic Gesture Recognition with Unsupervised Kinematic-Visual Data Alignment
In this paper, we propose a novel unsupervised domain adaptation framework which can simultaneously transfer multi-modality knowledge, i. e., both kinematic and visual data, from simulator to real robot.
Relational Graph Learning on Visual and Kinematics Embeddings for Accurate Gesture Recognition in Robotic Surgery
Automatic surgical gesture recognition is fundamentally important to enable intelligent cognitive assistance in robotic surgery.
Ranked #1 on
Action Segmentation
on JIGSAWS
Learning Motion Flows for Semi-supervised Instrument Segmentation from Robotic Surgical Video
Considering the fast instrument motion, we further introduce a flow compensator to estimate intermediate motion within continuous frames, with a novel cycle learning strategy.
LRTD: Long-Range Temporal Dependency based Active Learning for Surgical Workflow Recognition
Specifically, we propose a non-local recurrent convolutional network (NL-RCNet), which introduces non-local block to capture the long-range temporal dependency (LRTD) among continuous frames.
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.
Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated Fusion
We tackle this challenge and propose a novel multimodal segmentation framework which is robust to the absence of imaging modalities.
Automatic Gesture Recognition in Robot-assisted Surgery with Reinforcement Learning and Tree Search
Automatic surgical gesture recognition is fundamental for improving intelligence in robot-assisted surgery, such as conducting complicated tasks of surgery surveillance and skill evaluation.
Ranked #2 on
Action Segmentation
on JIGSAWS
Incorporating Temporal Prior from Motion Flow for Instrument Segmentation in Minimally Invasive Surgery Video
In this paper, we propose a novel framework to leverage instrument motion information, by incorporating a derived temporal prior to an attention pyramid network for accurate segmentation.
Multi-Task Recurrent Convolutional Network with Correlation Loss for Surgical Video Analysis
Mutually leveraging both low-level feature sharing and high-level prediction correlating, our MTRCNet-CL method can encourage the interactions between the two tasks to a large extent, and hence can bring about benefits to each other.
Ranked #3 on
Surgical tool detection
on Cholec80
Difficulty-aware Meta-learning for Rare Disease Diagnosis
Rare diseases have extremely low-data regimes, unlike common diseases with large amount of available labeled data.
Automated Pulmonary Nodule Detection via 3D ConvNets with Online Sample Filtering and Hybrid-Loss Residual Learning
In this paper, we propose a novel framework with 3D convolutional networks (ConvNets) for automated detection of pulmonary nodules from low-dose CT scans, which is a challenging yet crucial task for lung cancer early diagnosis and treatment.
3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes
Automatic liver segmentation from CT volumes is a crucial prerequisite yet challenging task for computer-aided hepatic disease diagnosis and treatment.
