49 Result(s)
within
Conference Paper
Qi Dou
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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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Chapter and Conference Paper
Test-Time Adaptation with Calibration of Medical Image Classification Nets for Label Distribution Shift
Class distribution plays an important role in learning deep classifiers. When the proportion of each class in the test set differs from the training set, the performance of classification nets usually degrades...
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Chapter and Conference Paper
WavTrans: Synergizing Wavelet and Cross-Attention Transformer for Multi-contrast MRI Super-Resolution
Current multi-contrast MRI super-resolution (SR) methods often harness convolutional neural networks (CNNs) for feature extraction and fusion. However, existing models have some shortcomings that prohibit them...
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Chapter and Conference Paper
Neural Rendering for Stereo 3D Reconstruction of Deformable Tissues in Robotic Surgery
Reconstruction of the soft tissues in robotic surgery from endoscopic stereo videos is important for many applications such as intra-operative navigation and image-guided robotic surgery automation. Previous w...
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Chapter and Conference Paper
Robust Cardiac MRI Segmentation with Data-Centric Models to Improve Performance via Intensive Pre-training and Augmentation
Segmentation of anatomical structures from Cardiac Magnetic Resonance (CMR) is central to the non-invasive quantitative assessment of cardiac function and structure, and deep-learning-based automatic segmentat...
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Chapter and Conference Paper
Flat-Aware Cross-Stage Distilled Framework for Imbalanced Medical Image Classification
Medical data often follow imbalanced distributions, which poses a long-standing challenge for computer-aided diagnosis systems built upon medical image classification. Most existing efforts are conducted by ap...
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Chapter and Conference Paper
DuDoCAF: Dual-Domain Cross-Attention Fusion with Recurrent Transformer for Fast Multi-contrast MR Imaging
Multi-contrast magnetic resonance imaging (MC-MRI) has been widely used for the diagnosis and characterization of tumors and lesions, as multi-contrast MR images are capable of providing complementary informat...
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Chapter and Conference Paper
AutoLaparo: A New Dataset of Integrated Multi-tasks for Image-guided Surgical Automation in Laparoscopic Hysterectomy
Computer-assisted minimally invasive surgery has great potential in benefiting modern operating theatres. The video data streamed from the endoscope provides rich information to support context-awareness for n...
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Chapter and Conference Paper
Efficient Federated Tumor Segmentation via Normalized Tensor Aggregation and Client Pruning
Federated learning, which trains a generic model for different institutions without sharing their data, is a new trend to avoid training with centralized data, which is often impossible due to privacy issues. ...
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Chapter and Conference Paper
Dynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class Imbalance
Despite recent progress on semi-supervised federated learning (FL) for medical image diagnosis, the problem of imbalanced class distributions among unlabeled clients is still unsolved for real-world use. In th...
