Search Results for author:
Found 63 papers, 31 papers with code
Single-Shot Plug-and-Play Methods for Inverse Problems
In this work, we introduce Single-Shot PnP methods (SS-PnP), shifting the focus to solving inverse problems with minimal data.
MuST: Multimodal Spatiotemporal Graph-Transformer for Hospital Readmission Prediction
Hospital readmission prediction is considered an essential approach to decreasing readmission rates, which is a key factor in assessing the quality and efficacy of a healthcare system.
FetusMapV2: Enhanced Fetal Pose Estimation in 3D Ultrasound
However, accurately estimating the 3D fetal pose in US volume has several challenges, including poor image quality, limited GPU memory for tackling high dimensional data, symmetrical or ambiguous anatomical structures, and considerable variations in fetal poses.
HIGT: Hierarchical Interaction Graph-Transformer for Whole Slide Image Analysis
Considering that the information from different resolutions is complementary and can benefit each other during the learning process, we further design a novel Bidirectional Interaction block to establish communication between different levels within the WSI pyramids.
Cheap Lunch for Medical Image Segmentation by Fine-tuning SAM on Few Exemplars
However, the adoption of foundational models in the medical domain presents a challenge due to the difficulty and expense of labeling sufficient data for adaptation within hospital systems.
ConSlide: Asynchronous Hierarchical Interaction Transformer with Breakup-Reorganize Rehearsal for Continual Whole Slide Image Analysis
In this paper, we propose the FIRST continual learning framework for WSI analysis, named ConSlide, to tackle the challenges of enormous image size, utilization of hierarchical structure, and catastrophic forgetting by progressive model updating on multiple sequential datasets.
Consistency-guided Meta-Learning for Bootstrapping Semi-Supervised Medical Image Segmentation
Specifically, our approach first involves training a segmentation model on a small set of clean labeled images to generate initial labels for unlabeled data.
Make-A-Volume: Leveraging Latent Diffusion Models for Cross-Modality 3D Brain MRI Synthesis
This paradigm extends the 2D image diffusion model to a volumetric version with a slightly increasing number of parameters and computation, offering a principled solution for generic cross-modality 3D medical image synthesis.
Histopathology Whole Slide Image Analysis with Heterogeneous Graph Representation Learning
We propose a novel heterogeneous graph-based framework to leverage the inter-relationships among different types of nuclei for WSI analysis.
Relabeling Minimal Training Subset to Flip a Prediction
When facing an unsatisfactory prediction from a machine learning model, it is crucial to investigate the underlying reasons and explore the potential for reversing the outcome.
HybridMIM: A Hybrid Masked Image Modeling Framework for 3D Medical Image Segmentation
Masked image modeling (MIM) with transformer backbones has recently been exploited as a powerful self-supervised pre-training technique.
Taming Diffusion Models for Audio-Driven Co-Speech Gesture Generation
In this work, we propose a novel diffusion-based framework, named Diffusion Co-Speech Gesture (DiffGesture), to effectively capture the cross-modal audio-to-gesture associations and preserve temporal coherence for high-fidelity audio-driven co-speech gesture generation.
MulGT: Multi-task Graph-Transformer with Task-aware Knowledge Injection and Domain Knowledge-driven Pooling for Whole Slide Image Analysis
Whole slide image (WSI) has been widely used to assist automated diagnosis under the deep learning fields.
MagicNet: Semi-Supervised Multi-Organ Segmentation via Magic-Cube Partition and Recovery
Our strategy encourages unlabeled images to learn organ semantics in relative locations from the labeled images (cross-branch) and enhances the learning ability for small organs (within-branch).
Domain-incremental Cardiac Image Segmentation with Style-oriented Replay and Domain-sensitive Feature Whitening
Particularly, we first present a style-oriented replay module to enable structure-realistic and memory-efficient reproduction of past data, and then incorporate the replayed past data to jointly optimize the model with current data to alleviate catastrophic forgetting.
Multi-Granularity Cross-modal Alignment for Generalized Medical Visual Representation Learning
In this paper, we present a novel Multi-Granularity Cross-modal Alignment (MGCA) framework for generalized medical visual representation learning by harnessing the naturally exhibited semantic correspondences between medical image and radiology reports at three different levels, i. e., pathological region-level, instance-level, and disease-level.
MCIBI++: Soft Mining Contextual Information Beyond Image for Semantic Segmentation
To this end, we propose a novel soft mining contextual information beyond image paradigm named MCIBI++ to further boost the pixel-level representations.
Joint Prediction of Meningioma Grade and Brain Invasion via Task-Aware Contrastive Learning
Preoperative and noninvasive prediction of the meningioma grade is important in clinical practice, as it directly influences the clinical decision making.
NestedFormer: Nested Modality-Aware Transformer for Brain Tumor Segmentation
Multi-modal MR imaging is routinely used in clinical practice to diagnose and investigate brain tumors by providing rich complementary information.
You Should Look at All Objects
Feature pyramid network (FPN) is one of the key components for object detectors.
Robust Medical Image Classification from Noisy Labeled Data with Global and Local Representation Guided Co-training
In this paper, we propose a novel collaborative training paradigm with global and local representation learning for robust medical image classification from noisy-labeled data to combat the lack of high quality annotated medical data.
CD$^2$-pFed: Cyclic Distillation-guided Channel Decoupling for Model Personalization in Federated Learning
Federated learning (FL) is a distributed learning paradigm that enables multiple clients to collaboratively learn a shared global model.
Learning to Bootstrap for Combating Label Noise
Specifically, our method dynamically adjusts the per-sample importance weight between the real observed labels and pseudo-labels, where the weights are efficiently determined in a meta process.
Ranked #8 on
Image Classification
on Clothing1M (using clean data)
(using extra training data)
CD2-pFed: Cyclic Distillation-Guided Channel Decoupling for Model Personalization in Federated Learning
Federated learning (FL) is a distributed learning paradigm that enables multiple clients to collaboratively learn a shared global model.
Rethinking Client Reweighting for Selfish Federated Learning
To this end, we study a new $\textit{selfish}$ variant of federated learning, in which the ultimate objective is to learn a model with optimal performance on internal clients $\textit{alone}$ instead of all clients.
Metal Artifact Reduction in 2D CT Images with Self-supervised Cross-domain Learning
We then design a novel FBP reconstruction loss to encourage the network to generate more perfect completion results and a residual-learning-based image refinement module to reduce the secondary artifacts in the reconstructed CT images.
All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentation
Observing this, we ask an unexplored but interesting question: can we exploit the unlabeled data via explicit real label supervision for semi-supervised training?
HCDG: A Hierarchical Consistency Framework for Domain Generalization on Medical Image Segmentation
Particularly, for the Extrinsic Consistency, we leverage the knowledge across multiple source domains to enforce data-level consistency.
nnFormer: Interleaved Transformer for Volumetric Segmentation
Transformer, the model of choice for natural language processing, has drawn scant attention from the medical imaging community.
Ranked #1 on
Medical Image Segmentation
on Synapse
CateNorm: Categorical Normalization for Robust Medical Image Segmentation
We propose a new normalization strategy, named categorical normalization (CateNorm), to normalize the activations according to categorical statistics.
TransCT: Dual-path Transformer for Low Dose Computed Tomography
Low dose computed tomography (LDCT) has attracted more and more attention in routine clinical diagnosis assessment, therapy planning, etc., which can reduce the dose of X-ray radiation to patients.
Dual-Teacher++: Exploiting Intra-domain and Inter-domain Knowledge with Reliable Transfer for Cardiac Segmentation
In this way, the dual teacher models would transfer acquired inter- and intra-domain knowledge to the student model for further integration and exploitation.
RMDL: Recalibrated multi-instance deep learning for whole slide gastric image classification
The multi-instance learning based on the most discriminative instances can be of great benefit for whole slide gastric image diagnosis.
DoFE: Domain-oriented Feature Embedding for Generalizable Fundus Image Segmentation on Unseen Datasets
Our DoFE framework dynamically enriches the image features with additional domain prior knowledge learned from multi-source domains to make the semantic features more discriminative.
Towards Cross-modality Medical Image Segmentation with Online Mutual Knowledge Distillation
Considering multi-modality data with the same anatomic structures are widely available in clinic routine, in this paper, we aim to exploit the prior knowledge (e. g., shape priors) learned from one modality (aka., assistant modality) to improve the segmentation performance on another modality (aka., target modality) to make up annotation scarcity.
Deep Sinogram Completion with Image Prior for Metal Artifact Reduction in CT Images
Computed tomography (CT) has been widely used for medical diagnosis, assessment, and therapy planning and guidance.
Self-supervised Feature Learning via Exploiting Multi-modal Data for Retinal Disease Diagnosis
The automatic diagnosis of various retinal diseases from fundus images is important to support clinical decision-making.
Learning from Extrinsic and Intrinsic Supervisions for Domain Generalization
To this end, we present a new domain generalization framework that learns how to generalize across domains simultaneously from extrinsic relationship supervision and intrinsic self-supervision for images from multi-source domains.
Dual-Teacher: Integrating Intra-domain and Inter-domain Teachers for Annotation-efficient Cardiac Segmentation
Medical image annotations are prohibitively time-consuming and expensive to obtain.
Multi-Site Infant Brain Segmentation Algorithms: The iSeg-2019 Challenge
Deep learning-based methods have achieved state-of-the-art performance; however, one of major limitations is that the learning-based methods may suffer from the multi-site issue, that is, the models trained on a dataset from one site may not be applicable to the datasets acquired from other sites with different imaging protocols/scanners.
Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation
Experiments on the NIH pancreas segmentation dataset and a multi-organ segmentation dataset show state-of-the-art performance of the proposed framework on semi-supervised medical image segmentation.
Deep Mining External Imperfect Data for Chest X-ray Disease Screening
Recent researches have demonstrated that performance bottleneck exists in joint training on different CXR datasets, and few made efforts to address the obstacle.
Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model
It is a consistency-based method which exploits the unlabeled data by encouraging the prediction consistency of given input under perturbations, and leverages a self-ensembling model to produce high-quality consistency targets for the unlabeled data.
MS-Net: Multi-Site Network for Improving Prostate Segmentation with Heterogeneous MRI Data
However, the prostate MRIs from different sites present heterogeneity due to the differences in scanners and imaging protocols, raising challenges for effective ways of aggregating multi-site data for network training.
CANet: Cross-disease Attention Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Grading
In this paper, we present a novel cross-disease attention network (CANet) to jointly grade DR and DME by exploring the internal relationship between the diseases with only image-level supervision.
Agent with Warm Start and Active Termination for Plane Localization in 3D Ultrasound
In this study, we propose a novel reinforcement learning (RL) framework to automatically localize fetal brain standard planes in 3D US.
Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation
We design a novel uncertainty-aware scheme to enable the student model to gradually learn from the meaningful and reliable targets by exploiting the uncertainty information.
Revisiting Metric Learning for Few-Shot Image Classification
However, the importance of feature embedding, i. e., exploring the relationship among training samples, is neglected.
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.
Boundary and Entropy-driven Adversarial Learning for Fundus Image Segmentation
The cross-domain discrepancy (domain shift) hinders the generalization of deep neural networks to work on different domain datasets. In this work, we present an unsupervised domain adaptation framework, called Boundary and Entropy-driven Adversarial Learning (BEAL), to improve the OD and OC segmentation performance, especially on the ambiguous boundary regions.
Unsupervised Detection of Distinctive Regions on 3D Shapes
This paper presents a novel approach to learn and detect distinctive regions on 3D shapes.
Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentation
In this paper, we present a novel semi-supervised method for medical image segmentation, where the network is optimized by the weighted combination of a common supervised loss for labeled inputs only and a regularization loss for both labeled and unlabeled data.
Patch-based Output Space Adversarial Learning for Joint Optic Disc and Cup Segmentation
In this paper, we present a novel patchbased Output Space Adversarial Learning framework (pOSAL) to jointly and robustly segment the OD and OC from different fundus image datasets.
Ranked #2 on
Optic Disc Segmentation
on REFUGE
3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training
Meanwhile, a fully-supervised method based on our approach achieved state-of-the-art performances on both the LiTS liver tumor segmentation and the Medical Segmentation Decathlon (MSD) challenge, demonstrating the robustness and value of our framework, even when fully supervised training is feasible.
Semi-supervised Skin Lesion Segmentation via Transformation Consistent Self-ensembling Model
In this paper, we present a novel semi-supervised method for skin lesion segmentation, where the network is optimized by the weighted combination of a common supervised loss for labeled inputs only and a regularization loss for both labeled and unlabeled data.
EC-Net: an Edge-aware Point set Consolidation Network
In this paper, we present the first deep learning based edge-aware technique to facilitate the consolidation of point clouds.
Deeply Supervised Rotation Equivariant Network for Lesion Segmentation in Dermoscopy Images
Our best model achieves 77. 23\%(JA) on the test dataset, outperforming the state-of-the-art challenging methods and further demonstrating the effectiveness of our proposed deeply supervised rotation equivariant segmentation network.
PU-Net: Point Cloud Upsampling Network
Learning and analyzing 3D point clouds with deep networks is challenging due to the sparseness and irregularity of the data.
Ranked #3 on
Point Cloud Super Resolution
on SHREC15
Automatic 3D Cardiovascular MR Segmentation with Densely-Connected Volumetric ConvNets
Second, it avoids learning redundant feature maps by encouraging feature reuse and hence requires fewer parameters to achieve high performance, which is essential for medical applications with limited training data.
Fine-grained Recurrent Neural Networks for Automatic Prostate Segmentation in Ultrasound Images
Additionally, our approach is general and can be extended to other medical image segmentation tasks, where boundary incompleteness is one of the main challenges.
VoxResNet: Deep Voxelwise Residual Networks for Volumetric Brain Segmentation
Recently deep residual learning with residual units for training very deep neural networks advanced the state-of-the-art performance on 2D image recognition tasks, e. g., object detection and segmentation.
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.
DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation
The morphology of glands has been used routinely by pathologists to assess the malignancy degree of adenocarcinomas.
Ranked #3 on
Optic Disc Segmentation
on REFUGE
