Search Results for author:
Found 60 papers, 35 papers with code
Bridge the Gap Between Visual and Linguistic Comprehension for Generalized Zero-shot Semantic Segmentation
In SPMatch, we comprehend objects with spatial part information from both visual and linguistic perspectives and perform graph matching to bridge the gap.
DSPFusion: Image Fusion via Degradation and Semantic Dual-Prior Guidance
This work presents a \textbf{D}egradation and \textbf{S}emantic \textbf{P}rior dual-guided framework for degraded image \textbf{Fusion} (\textbf{DSPFusion}), utilizing degradation priors and high-quality scene semantic priors restored via diffusion models to guide both information recovery and fusion in a unified model.
X$^{2}$-Gaussian: 4D Radiative Gaussian Splatting for Continuous-time Tomographic Reconstruction
By unifying continuous motion modeling with hardware-free period learning, X$^2$-Gaussian advances high-fidelity 4D CT reconstruction for dynamic clinical imaging.
GeoT: Geometry-guided Instance-dependent Transition Matrix for Semi-supervised Tooth Point Cloud Segmentation
Achieving meticulous segmentation of tooth point clouds from intra-oral scans stands as an indispensable prerequisite for various orthodontic applications.
MAP: Evaluation and Multi-Agent Enhancement of Large Language Models for Inpatient Pathways
Then, we proposed the Multi-Agent Inpatient Pathways (MAP) framework to accomplish inpatient pathways with three clinical agents, including a triage agent managing the patient admission, a diagnosis agent serving as the primary decision maker at the department, and a treatment agent providing treatment plans.
CREATE-FFPE: Cross-Resolution Compensated and Multi-Frequency Enhanced FS-to-FFPE Stain Transfer for Intraoperative IHC Images
In the immunohistochemical (IHC) analysis during surgery, frozen-section (FS) images are used to determine the benignity or malignancy of the tumor.
FedBM: Stealing Knowledge from Pre-trained Language Models for Heterogeneous Federated Learning
However, current studies have shown that data heterogeneity incurs local learning bias in classifiers and feature extractors of client models during local training, leading to the performance degradation of a federation system.
A Survey of LLM-based Agents in Medicine: How far are we from Baymax?
Large Language Models (LLMs) are transforming healthcare through the development of LLM-based agents that can understand, reason about, and assist with medical tasks.
Polyp-Gen: Realistic and Diverse Polyp Image Generation for Endoscopic Dataset Expansion
Despite recent advancements in generating synthetic images for dataset expansion, existing endoscopic image generation algorithms failed to accurately generate the details of polyp boundary regions and typically required medical priors to specify plausible locations and shapes of polyps, which limited the realism and diversity of the generated images.
ConcealGS: Concealing Invisible Copyright Information in 3D Gaussian Splatting
With the rapid development of 3D reconstruction technology, the widespread distribution of 3D data has become a future trend.
DEeR: Deviation Eliminating and Noise Regulating for Privacy-preserving Federated Low-rank Adaptation
Furthermore, we also conduct an in-depth analysis of the noise amplification effect and find that this problem is mainly caused by the ``linear relationship'' between DP noise and LoRA parameters.
AutoPET Challenge: Tumour Synthesis for Data Augmentation
Accurate lesion segmentation in whole-body PET/CT scans is crucial for cancer diagnosis and treatment planning, but limited datasets often hinder the performance of automated segmentation models.
When 3D Partial Points Meets SAM: Tooth Point Cloud Segmentation with Sparse Labels
To demonstrate the effectiveness of the framework, we conduct experiments on the public dataset and surprisingly find with only 0. 1\% annotations (one point per tooth), our method can surpass recent weakly supervised methods by a large margin, and the performance is even comparable to the recent fully-supervised methods, showcasing the significant potential of applying SAM to 3D perception tasks with sparse labels.
DiffRect: Latent Diffusion Label Rectification for Semi-supervised Medical Image Segmentation
DiffRect first utilizes a Label Context Calibration Module (LCC) to calibrate the biased relationship between classes by learning the category-wise correlation in pseudo labels, then apply Latent Feature Rectification Module (LFR) on the latent space to formulate and align the pseudo label distributions of different levels via latent diffusion.
Exploiting Scale-Variant Attention for Segmenting Small Medical Objects
Early detection and accurate diagnosis can predict the risk of malignant disease transformation, thereby increasing the probability of effective treatment.
GTP-4o: Modality-prompted Heterogeneous Graph Learning for Omni-modal Biomedical Representation
To tackle these, we propose an innovative Modality-prompted Heterogeneous Graph for Omnimodal Learning (GTP-4o), which embeds the numerous disparate clinical modalities into a unified representation, completes the deficient embedding of missing modality and reformulates the cross-modal learning with a graph-based aggregation.
GaussianStego: A Generalizable Stenography Pipeline for Generative 3D Gaussians Splatting
Recent advancements in large generative models and real-time neural rendering using point-based techniques pave the way for a future of widespread visual data distribution through sharing synthesized 3D assets.
EndoSparse: Real-Time Sparse View Synthesis of Endoscopic Scenes using Gaussian Splatting
3D reconstruction of biological tissues from a collection of endoscopic images is a key to unlock various important downstream surgical applications with 3D capabilities.
Spatial-temporal Hierarchical Reinforcement Learning for Interpretable Pathology Image Super-Resolution
Specifically, the higher-level spatial manager is proposed to pick out the most corrupted patch for the lower-level patch worker.
LGS: A Light-weight 4D Gaussian Splatting for Efficient Surgical Scene Reconstruction
However, the prerequisite for modeling dynamic scenes by a large number of Gaussian units, the high-dimensional Gaussian attributes and the high-resolution deformation fields, all lead to serve storage issues that hinder real-time rendering in resource-limited surgical equipment.
Learning to Adapt Foundation Model DINOv2 for Capsule Endoscopy Diagnosis
Our method, inspired by the DINOv2 foundation model, applies low-rank adaptation learning to tailor foundation models for capsule endoscopy diagnosis effectively.
U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation
We further delved into the potential of U-KAN as an alternative U-Net noise predictor in diffusion models, demonstrating its applicability in generating task-oriented model architectures.
Universal and Extensible Language-Vision Models for Organ Segmentation and Tumor Detection from Abdominal Computed Tomography
However, these AI models often struggle with flexibility for partially annotated datasets and extensibility for new classes due to limitations in the one-hot encoding, architectural design, and learning scheme.
Endora: Video Generation Models as Endoscopy Simulators
In a nutshell, Endora marks a notable breakthrough in the deployment of generative AI for clinical endoscopy research, setting a substantial stage for further advances in medical content generation.
Medical Image Synthesis via Fine-Grained Image-Text Alignment and Anatomy-Pathology Prompting
To address these challenges, we propose a novel medical image synthesis model that leverages fine-grained image-text alignment and anatomy-pathology prompts to generate highly detailed and accurate synthetic medical images.
UN-SAM: Universal Prompt-Free Segmentation for Generalized Nuclei Images
Moreover, to unleash the generalization capability of SAM across a variety of nuclei images, we devise a Domain-adaptive Tuning Encoder (DT-Encoder) to seamlessly harmonize visual features with domain-common and domain-specific knowledge, and further devise a Domain Query-enhanced Decoder (DQ-Decoder) by leveraging learnable domain queries for segmentation decoding in different nuclei domains.
EndoGaussian: Real-time Gaussian Splatting for Dynamic Endoscopic Scene Reconstruction
To adapt 3DGS for endoscopic scenes, we propose two strategies, Holistic Gaussian Initialization (HGI) and Spatio-temporal Gaussian Tracking (SGT), to handle the non-trivial Gaussian initialization and tissue deformation problems, respectively.
Self-supervised Learning of LiDAR 3D Point Clouds via 2D-3D Neural Calibration
First, we propose the learnable transformation alignment to bridge the domain gap between image and point cloud data, converting features into a unified representation space for effective comparison and matching.
Fine-Grained Image-Text Alignment in Medical Imaging Enables Explainable Cyclic Image-Report Generation
To address these issues, we propose a novel Adaptive patch-word Matching (AdaMatch) model to correlate chest X-ray (CXR) image regions with words in medical reports and apply it to CXR-report generation to provide explainability for the generation process.
Alternate Diverse Teaching for Semi-supervised Medical Image Segmentation
To mitigate the confirmation bias from the diverse supervision, the core of AD-MT lies in two proposed modules: the Random Periodic Alternate (RPA) Updating Module and the Conflict-Combating Module (CCM).
Holistic Evaluation of GPT-4V for Biomedical Imaging
GPT-4V represents a breakthrough in artificial general intelligence (AGI) for computer vision, with applications in the biomedical domain.
Rethinking Data Perturbation and Model Stabilization for Semi-supervised Medical Image Segmentation
Studies on semi-supervised medical image segmentation (SSMIS) have seen fast progress recently.
Review of Large Vision Models and Visual Prompt Engineering
This review aims to summarize the methods employed in the computer vision domain for large vision models and visual prompt engineering, exploring the latest advancements in visual prompt engineering.
Artificial General Intelligence for Medical Imaging Analysis
Large-scale Artificial General Intelligence (AGI) models, including Large Language Models (LLMs) such as ChatGPT/GPT-4, have achieved unprecedented success in a variety of general domain tasks.
EfficientViT: Memory Efficient Vision Transformer with Cascaded Group Attention
Comprehensive experiments demonstrate EfficientViT outperforms existing efficient models, striking a good trade-off between speed and accuracy.
Instruction-ViT: Multi-Modal Prompts for Instruction Learning in ViT
Prompts have been proven to play a crucial role in large language models, and in recent years, vision models have also been using prompts to improve scalability for multiple downstream tasks.
Prompt Engineering for Healthcare: Methodologies and Applications
Prompt engineering is a critical technique in the field of natural language processing that involves designing and optimizing the prompts used to input information into models, aiming to enhance their performance on specific tasks.
Unleash the Potential of Image Branch for Cross-modal 3D Object Detection
To achieve reliable and precise scene understanding, autonomous vehicles typically incorporate multiple sensing modalities to capitalize on their complementary attributes.
CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection
The proposed model is developed from an assembly of 14 datasets, using a total of 3, 410 CT scans for training and then evaluated on 6, 162 external CT scans from 3 additional datasets.
Ranked #1 on
Organ Segmentation
on BTCV
FedPD: Federated Open Set Recognition with Parameter Disentanglement
To this end, we aim to study a novel problem of federated open-set recognition (FedOSR), which learns an open-set recognition (OSR) model under federated paradigm such that it classifies seen classes while at the same time detects unknown classes.
Novel Scenes & Classes: Towards Adaptive Open-set Object Detection
Then, a high-order metric is proposed to match the most significant motif as high-order patterns, serving for motif-guided novel-class learning.
MRM: Masked Relation Modeling for Medical Image Pre-Training with Genetics
Moreover, to enhance semantic relation modeling, we propose relation matching to align the sample-wise relation between the intact and masked features.
Adjustment and Alignment for Unbiased Open Set Domain Adaptation
In a nutshell, ANNA consists of Front-Door Adjustment (FDA) to correct the biased learning in the source domain and Decoupled Causal Alignment (DCA) to transfer the model unbiasedly.
A Comprehensive Study of the Robustness for LiDAR-based 3D Object Detectors against Adversarial Attacks
Specifically, we extend three distinct adversarial attacks to the 3D object detection task, benchmarking the robustness of state-of-the-art LiDAR-based 3D object detectors against attacks on the KITTI and Waymo datasets.
Biomedical image analysis competitions: The state of current participation practice
Of these, 84% were based on standard architectures.
A heterogeneous group CNN for image super-resolution
To prevent loss of original information, a multi-level enhancement mechanism guides a CNN to achieve a symmetric architecture for promoting expressive ability of HGSRCNN.
GLENet: Boosting 3D Object Detectors with Generative Label Uncertainty Estimation
The label uncertainty generated by GLENet is a plug-and-play module and can be conveniently integrated into existing deep 3D detectors to build probabilistic detectors and supervise the learning of the localization uncertainty.
Ranked #2 on
3D Object Detection
on KITTI Cars Easy
Joint Class-Affinity Loss Correction for Robust Medical Image Segmentation with Noisy Labels
Noisy labels collected with limited annotation cost prevent medical image segmentation algorithms from learning precise semantic correlations.
Image Super-resolution with An Enhanced Group Convolutional Neural Network
In this paper, we present an enhanced super-resolution group CNN (ESRGCNN) with a shallow architecture by fully fusing deep and wide channel features to extract more accurate low-frequency information in terms of correlations of different channels in single image super-resolution (SISR).
Towards Robust Adaptive Object Detection under Noisy Annotations
Domain Adaptive Object Detection (DAOD) models a joint distribution of images and labels from an annotated source domain and learns a domain-invariant transformation to estimate the target labels with the given target domain images.
SimT: Handling Open-set Noise for Domain Adaptive Semantic Segmentation
By exploiting computational geometry analysis and properties of segmentation, we design three complementary regularizers, i. e. volume regularization, anchor guidance, convex guarantee, to approximate the true SimT.
IDEA-Net: Dynamic 3D Point Cloud Interpolation via Deep Embedding Alignment
This paper investigates the problem of temporally interpolating dynamic 3D point clouds with large non-rigid deformation.
SIGMA: Semantic-complete Graph Matching for Domain Adaptive Object Detection
To overcome these challenges, we propose a novel SemantIc-complete Graph MAtching (SIGMA) framework for DAOD, which completes mismatched semantics and reformulates the adaptation with graph matching.
Incremental Cross-view Mutual Distillation for Self-supervised Medical CT Synthesis
Under this circumstance, the models learned from different views can distill valuable knowledge to guide the learning processes of each other.
Exploring Gradient Flow Based Saliency for DNN Model Compression
Model pruning aims to reduce the deep neural network (DNN) model size or computational overhead.
Personalized Retrogress-Resilient Framework for Real-World Medical Federated Learning
To address this problem, we propose a personalized retrogress-resilient framework to produce a superior personalized model for each client.
Categorical Relation-Preserving Contrastive Knowledge Distillation for Medical Image Classification
With the contribution of the CCD and CRP, our CRCKD algorithm can distill the relational knowledge more comprehensively.
MetaCorrection: Domain-aware Meta Loss Correction for Unsupervised Domain Adaptation in Semantic Segmentation
Existing self-training based UDA approaches assign pseudo labels for target data and treat them as ground truth labels to fully leverage unlabeled target data for model adaptation.
Ranked #32 on
Synthetic-to-Real Translation
on SYNTHIA-to-Cityscapes
Complementary Network with Adaptive Receptive Fields for Melanoma Segmentation
To tackle these issues, we propose a novel complementary network with adaptive receptive filed learning.
Domain Knowledge Based Brain Tumor Segmentation and Overall Survival Prediction
Automatically segmenting sub-regions of gliomas (necrosis, edema and enhancing tumor) and accurately predicting overall survival (OS) time from multimodal MRI sequences have important clinical significance in diagnosis, prognosis and treatment of gliomas.
