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Found 37 papers, 16 papers with code
JOINEDTrans: Prior Guided Multi-task Transformer for Joint Optic Disc/Cup Segmentation and Fovea Detection
To address these issues, we propose a prior guided multi-task transformer framework for joint OD/OC segmentation and fovea detection, named JOINEDTrans.
MProtoNet: A Case-Based Interpretable Model for Brain Tumor Classification with 3D Multi-parametric Magnetic Resonance Imaging
Recent applications of deep convolutional neural networks in medical imaging raise concerns about their interpretability.
Unifying and Personalizing Weakly-supervised Federated Medical Image Segmentation via Adaptive Representation and Aggregation
The statistical heterogeneity (e. g., non-IID data and domain shifts) is a primary obstacle in FL, impairing the generalization performance of the global model.
Learning Enhancement From Degradation: A Diffusion Model For Fundus Image Enhancement
In this paper, we introduce a novel diffusion model based framework, named Learning Enhancement from Degradation (LED), for enhancing fundus images.
FedEBA+: Towards Fair and Effective Federated Learning via Entropy-Based Model
Ensuring fairness is a crucial aspect of Federated Learning (FL), which enables the model to perform consistently across all clients.
FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clustering
In this paper, we identify the learning challenges posed by the simultaneous occurrence of diverse distribution shifts and propose a clustering principle to overcome these challenges.
Network analysis on cortical morphometry in first-episode schizophrenia
Our work supports that cortical morphological connectivity, which is constructed based on correlations across subjects' cortical thickness, may serve as a tool to study topological abnormalities in neurological disorders.
UNO-QA: An Unsupervised Anomaly-Aware Framework with Test-Time Clustering for OCTA Image Quality Assessment
Medical image quality assessment (MIQA) is a vital prerequisite in various medical image analysis applications.
YoloCurvSeg: You Only Label One Noisy Skeleton for Vessel-style Curvilinear Structure Segmentation
With only one noisy skeleton annotation (respectively 0. 14%, 0. 03%, 1. 40%, and 0. 65% of the full annotation), YoloCurvSeg achieves more than 97% of the fully-supervised performance on each dataset.
SSiT: Saliency-guided Self-supervised Image Transformer for Diabetic Retinopathy Grading
Specifically, two saliency-guided learning tasks are employed in SSiT: (1) We conduct saliency-guided contrastive learning based on the momentum contrast, wherein we utilize fundus images' saliency maps to remove trivial patches from the input sequences of the momentum-updated key encoder.
Diversity Boosted Learning for Domain Generalization with Large Number of Domains
Machine learning algorithms minimizing the average training loss usually suffer from poor generalization performance due to the greedy exploitation of correlations among the training data, which are not stable under distributional shifts.
AADG: Automatic Augmentation for Domain Generalization on Retinal Image Segmentation
To address this issue, we propose a data manipulation based domain generalization method, called Automated Augmentation for Domain Generalization (AADG).
DELTA: Diverse Client Sampling for Fasting Federated Learning
However, an inadequate client sampling scheme can lead to the selection of unrepresentative subsets, resulting in significant variance in model updates and slowed convergence.
FedDebias: Reducing the Local Learning Bias Improves Federated Learning on Heterogeneous Data
As a remedy, we propose FedDebias, a novel unified algorithm that reduces the local learning bias on features and classifiers to tackle these challenges.
DS3-Net: Difficulty-perceived Common-to-T1ce Semi-Supervised Multimodal MRI Synthesis Network
Furthermore, with only 5% paired data, the proposed DS3-Net achieves competitive performance with state-of-theart image translation methods utilizing 100% paired data, delivering an average SSIM of 0. 8947 and an average PSNR of 23. 60.
LesionPaste: One-Shot Anomaly Detection for Medical Images
Then, MixUp is adopted to paste patches from the lesion bank at random positions in normal images to synthesize anomalous samples for training.
Semi-supervised Anomaly Detection
supervised anomaly detection
Uni4Eye: Unified 2D and 3D Self-supervised Pre-training via Masked Image Modeling Transformer for Ophthalmic Image Classification
We employ a Unified Patch Embedding module to replace the origin patch embedding module in ViT for jointly processing both 2D and 3D input images.
Multi-modal Brain Tumor Segmentation via Missing Modality Synthesis and Modality-level Attention Fusion
As such, it is clinically meaningful to develop a method to synthesize unavailable modalities which can also be used as additional inputs to downstream tasks (e. g., brain tumor segmentation) for performance enhancing.
Student Becomes Decathlon Master in Retinal Vessel Segmentation via Dual-teacher Multi-target Domain Adaptation
Afterwards, knowledge distillation is performed to iteratively distill different domain knowledge from teachers to a generic student.
JOINED : Prior Guided Multi-task Learning for Joint Optic Disc/Cup Segmentation and Fovea Detection
At the coarse stage, we obtain the OD/OC coarse segmentation and the heatmap localization of fovea through a joint segmentation and detection module.
A Holistic Review on Advanced Bi-directional EV Charging Control Algorithms
The rapid growth of electric vehicles (EVs) has promised a next-generation transportation system with reduced carbon emission.
GAMMA Challenge:Glaucoma grAding from Multi-Modality imAges
However, although numerous algorithms are proposed based on fundus images or OCT volumes in computer-aided diagnosis, there are still few methods leveraging both of the modalities for the glaucoma assessment.
Unsupervised Domain Adaptation for Cross-Modality Retinal Vessel Segmentation via Disentangling Representation Style Transfer and Collaborative Consistency Learning
The two models use labeled data (together with the corresponding transferred images) for supervised learning and perform collaborative consistency learning on unlabeled data.
COROLLA: An Efficient Multi-Modality Fusion Framework with Supervised Contrastive Learning for Glaucoma Grading
In this paper, we propose an efficient multi-modality supervised contrastive learning framework, named COROLLA, for glaucoma grading.
Towards Federated Learning on Time-Evolving Heterogeneous Data
Federated Learning (FL) is a learning paradigm that protects privacy by keeping client data on edge devices.
Identifying the key components in ResNet-50 for diabetic retinopathy grading from fundus images: a systematic investigation
To identify the key components in a standard deep learning framework (ResNet-50) for DR grading, we systematically analyze the impact of several major components.
LDDMM-Face: Large Deformation Diffeomorphic Metric Learning for Cross-annotation Face Alignment
We innovatively propose a flexible and consistent cross-annotation face alignment framework, LDDMM-Face, the key contribution of which is a deformation layer that naturally embeds facial geometry in a diffeomorphic way.
Automatic hippocampal surface generation via 3D U-net and active shape modeling with hybrid particle swarm optimization
The hippocampal surface was then generated from the mean shape and the shape variation parameters.
LDDMM-Face: Large Deformation Diffeomorphic Metric Learning for Flexible and Consistent Face Alignment
Instead of predicting facial landmarks via heatmap or coordinate regression, we formulate this task in a diffeomorphic registration manner and predict momenta that uniquely parameterize the deformation between initial boundary and true boundary, and then perform large deformation diffeomorphic metric mapping (LDDMM) simultaneously for curve and landmark to localize the facial landmarks.
Lesion-based Contrastive Learning for Diabetic Retinopathy Grading from Fundus Images
Instead of taking entire images as the input in the common contrastive learning scheme, lesion patches are employed to encourage the feature extractor to learn representations that are highly discriminative for DR grading.
BSDA-Net: A Boundary Shape and Distance Aware Joint Learning Framework for Segmenting and Classifying OCTA Images
Moreover, both low-level and high-level features from the aforementioned three branches, including shape, size, boundary, and signed directional distance map of FAZ, are fused hierarchically with features from the diagnostic classifier.
DRR4Covid: Learning Automated COVID-19 Infection Segmentation from Digitally Reconstructed Radiographs
The infection-aware DRR generator is able to produce DRRs with adjustable strength of radiological signs of COVID-19 infection, and generate pixel-level infection annotations that match the DRRs precisely.
Learning Diagnosis of COVID-19 from a Single Radiological Image
To address this problem, we explore the feasibility of learning deep models for COVID-19 diagnosis from a single radiological image by resorting to synthesizing diverse radiological images.
A Survey on Deep Learning of Small Sample in Biomedical Image Analysis
In order to accelerate the clinical usage of biomedical image analysis based on deep learning techniques, we intentionally expand this survey to include the explanation methods for deep models that are important to clinical decision making.
Brain segmentation based on multi-atlas guided 3D fully convolutional network ensembles
To address this limitation, we trained a 3D FCN model for each ROI using patches of adaptive size and embedded outputs of the convolutional layers in the deconvolutional layers to further capture the local and global context patterns.
A post-processing method to improve the white matter hyperintensity segmentation accuracy for randomly-initialized U-net
Random initialization is usally used to initialize the model weights in the U-net.
Prostate Segmentation using 2D Bridged U-net
To address the aforementioned three problems, we propose and validate a deeper network that can fit medical image datasets that are usually small in the sample size.
