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Article
Open AccessEfficient EndoNeRF reconstruction and its application for data-driven surgical simulation
The healthcare industry has a growing need for realistic modeling and efficient simulation of surgical scenes. With effective models of deformable surgical scenes, clinicians are able to conduct surgical plann...
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Article
Open AccessIntelligent surgical workflow recognition for endoscopic submucosal dissection with real-time animal study
Recent advancements in artificial intelligence have witnessed human-level performance; however, AI-enabled cognitive assistance for therapeutic procedures has not been fully explored nor pre-clinically validat...
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Article
IJCARS-IPCAI 2023 special issue: conference information processing for computer-assisted interventions, 14th International Conference 2023—part 1
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Article
Open AccessAuthor Correction: Federated learning enables big data for rare cancer boundary detection
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Chapter and Conference Paper
Client-Level Differential Privacy via Adaptive Intermediary in Federated Medical Imaging
Despite recent progress in enhancing the privacy of federated learning (FL) via differential privacy (DP), the trade-off of DP between privacy protection and performance is still underexplored for real-world m...
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Chapter and Conference Paper
Treatment Outcome Prediction for Intracerebral Hemorrhage via Generative Prognostic Model with Imaging and Tabular Data
Intracerebral hemorrhage (ICH) is the second most common and deadliest form of stroke. Despite medical advances, predicting treatment outcomes for ICH remains a challenge. This paper proposes a novel prognosti...
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Chapter and Conference Paper
ArSDM: Colonoscopy Images Synthesis with Adaptive Refinement Semantic Diffusion Models
Colonoscopy analysis, particularly automatic polyp segmentation and detection, is essential for assisting clinical diagnosis and treatment. However, as medical image annotation is labour- and resource-intensiv...
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Chapter and Conference Paper
Foundation Model for Endoscopy Video Analysis via Large-Scale Self-supervised Pre-train
Foundation models have exhibited remarkable success in various applications, such as disease diagnosis and text report generation. To date, a foundation model for endoscopic video analysis is still lacking. In...
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Chapter and Conference Paper
Fast Non-Markovian Diffusion Model for Weakly Supervised Anomaly Detection in Brain MR Images
In medical image analysis, anomaly detection in weakly supervised settings has gained significant interest due to the high cost associated with expert-annotated pixel-wise labeling. Current methods primarily r...
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Chapter and Conference Paper
Imitation Learning from Expert Video Data for Dissection Trajectory Prediction in Endoscopic Surgical Procedure
High-level cognitive assistance, such as predicting dissection trajectories in Endoscopic Submucosal Dissection (ESD), can potentially support and facilitate surgical skills training. However, it has rarely be...
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Chapter and Conference Paper
Learning Robust Classifier for Imbalanced Medical Image Dataset with Noisy Labels by Minimizing Invariant Risk
In medical image analysis, imbalanced noisy dataset classification poses a long-standing and critical problem since clinical large-scale datasets often attain noisy labels and imbalanced distributions through ...
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Chapter and Conference Paper
Efficient Federated Tumor Segmentation via Parameter Distance Weighted Aggregation and Client Pruning
Federated learning has become a popular paradigm to enable multiple distributed clients collaboratively train a model, providing a promising privacy-preserving solution without data sharing. To fully make use ...
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Chapter and Conference Paper
On Fairness of Medical Image Classification with Multiple Sensitive Attributes via Learning Orthogonal Representations
Mitigating the discrimination of machine learning models has gained increasing attention in medical image analysis. However, rare works focus on fair treatments for patients with multiple sensitive demographic...
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Chapter and Conference Paper
FedSoup: Improving Generalization and Personalization in Federated Learning via Selective Model Interpolation
Cross-silo federated learning (FL) enables the development of machine learning models on datasets distributed across data centers such as hospitals and clinical research laboratories. However, recent research ...
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Chapter and Conference Paper
Diffusion Model Based Semi-supervised Learning on Brain Hemorrhage Images for Efficient Midline Shift Quantification
Brain midline shift (MLS) is one of the most critical factors to be considered for clinical diagnosis and treatment decision-making for intracranial hemorrhage. Existing computational methods on MLS quantifica...
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Article
Open AccessFederated learning enables big data for rare cancer boundary detection
Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging...
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Article
Trans-SVNet: hybrid embedding aggregation Transformer for surgical workflow analysis
Real-time surgical workflow analysis has been a key component for computer-assisted intervention system to improve cognitive assistance. Most existing methods solely rely on conventional temporal models and en...
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Article
Morphology-aware multi-source fusion–based intracranial aneurysms rupture prediction
We proposed a new approach to train deep learning model for aneurysm rupture prediction which only uses a limited amount of labeled data.
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Article
Autonomous environment-adaptive microrobot swarm navigation enabled by deep learning-based real-time distribution planning
Navigating a large swarm of micro-/nanorobots is critical for potential targeted delivery/therapy applications owing to the limited volume/function of a single microrobot, and microrobot swarms with distributi...
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Article
Open AccessAuthor Correction: Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study
