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Found 180 papers, 97 papers with code
Partially Supervised Unpaired Multi-Modal Learning for Label-Efficient Medical Image Segmentation
Specifically, our framework consists of a compact segmentation network with modality specific normalization layers for learning with partially labeled unpaired multi-modal data.
Vision-Language Model IP Protection via Prompt-based Learning
This branch integrates self-enhanced and cross-domain features, further strengthening IP-CLIP's capability to block features from unauthorized domains.
Hierarchical Context Transformer for Multi-level Semantic Scene Understanding
A comprehensive and explicit understanding of surgical scenes plays a vital role in developing context-aware computer-assisted systems in the operating theatre.
ViLa-MIL: Dual-scale Vision-Language Multiple Instance Learning for Whole Slide Image Classification
Specifically, we propose a dual-scale visual descriptive text prompt based on the frozen large language model (LLM) to boost the performance of VLM effectively.
MM-Retinal V2: Transfer an Elite Knowledge Spark into Fundus Vision-Language Pretraining
Vision-language pretraining (VLP) has been investigated to generalize across diverse downstream tasks for fundus image analysis.
Fundus Image Quality Assessment and Enhancement: a Systematic Review
As an affordable and convenient eye scan, fundus photography holds the potential for preventing vision impairment, especially in resource-limited regions.
No More Sliding Window: Efficient 3D Medical Image Segmentation with Differentiable Top-k Patch Sampling
3D models surpass 2D models in CT/MRI segmentation by effectively capturing inter-slice relationships.
AIF-SFDA: Autonomous Information Filter-driven Source-Free Domain Adaptation for Medical Image Segmentation
Decoupling domain-variant information (DVI) from domain-invariant information (DII) serves as a prominent strategy for mitigating domain shifts in the practical implementation of deep learning algorithms.
Look Back for More: Harnessing Historical Sequential Updates for Personalized Federated Adapter Tuning
In pFedSeq, the server maintains and trains a sequential learner, which processes a sequence of past adapter updates from clients and generates calibrations for personalized adapters.
Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation
In this paper, we propose a learnable prompting SAM-induced Knowledge distillation framework (KnowSAM) for semi-supervised medical image segmentation.
GEMeX: A Large-Scale, Groundable, and Explainable Medical VQA Benchmark for Chest X-ray Diagnosis
However, current medical VQA datasets exhibit two significant limitations: (1) they often lack visual and textual explanations for answers, which impedes their ability to satisfy the comprehension needs of patients and junior doctors; (2) they typically offer a narrow range of question formats, inadequately reflecting the diverse requirements encountered in clinical scenarios.
Out-Of-Distribution Detection with Diversification (Provably)
Out-of-distribution (OOD) detection is crucial for ensuring reliable deployment of machine learning models.
DB-SAM: Delving into High Quality Universal Medical Image Segmentation
On 21 3D medical image segmentation tasks, our proposed DB-SAM achieves an absolute gain of 8. 8%, compared to a recent medical SAM adapter in the literature.
Serp-Mamba: Advancing High-Resolution Retinal Vessel Segmentation with Selective State-Space Model
Based on the above observations, we first devise a Serpentine Interwoven Adaptive (SIA) scan mechanism, which scans UWF-SLO images along curved vessel structures in a snake-like crawling manner.
MedSAM-U: Uncertainty-Guided Auto Multi-Prompt Adaptation for Reliable MedSAM
The Medical Segment Anything Model (MedSAM) has shown remarkable performance in medical image segmentation, drawing significant attention in the field.
Say No to Freeloader: Protecting Intellectual Property of Your Deep Model
Based on the proposed whole method, the novel style and discriminative loss functions are designed to effectively enhance the distinction in style and discriminative features between authorized and unauthorized domains, respectively.
Beyond the Eye: A Relational Model for Early Dementia Detection Using Retinal OCTA Images
A regional relationship module is applied after the multi-view module to excavate the relationship between the sub-regions.
CLIP-DR: Textual Knowledge-Guided Diabetic Retinopathy Grading with Ranking-aware Prompting
Diabetic retinopathy (DR) is a complication of diabetes and usually takes decades to reach sight-threatening levels.
STLLaVA-Med: Self-Training Large Language and Vision Assistant for Medical Question-Answering
Large Vision-Language Models (LVLMs) have shown significant potential in assisting medical diagnosis by leveraging extensive biomedical datasets.
ViDSOD-100: A New Dataset and a Baseline Model for RGB-D Video Salient Object Detection
However, the existing salient object detection (SOD) works only focus on either static RGB-D images or RGB videos, ignoring the collaborating of RGB-D and video information.
Common and Rare Fundus Diseases Identification Using Vision-Language Foundation Model with Knowledge of Over 400 Diseases
Here we introduce RetiZero, a vision-language foundation model that leverages knowledge from over 400 fundus diseases.
Towards Federated Domain Unlearning: Verification Methodologies and Challenges
Federated Learning (FL) has evolved as a powerful tool for collaborative model training across multiple entities, ensuring data privacy in sensitive sectors such as healthcare and finance.
Confidence-Based Task Prediction in Continual Disease Classification Using Probability Distribution
We present CTP, a task-id predictor that utilizes confidence scores, leveraging the probability distribution (logits) of the classifier to accurately determine the task-id at inference time.
Confidence-aware multi-modality learning for eye disease screening
In this study, we propose a novel multi-modality evidential fusion pipeline for eye disease screening.
Memory-efficient High-resolution OCT Volume Synthesis with Cascaded Amortized Latent Diffusion Models
In tandem with autoencoders, we propose cascaded diffusion processes to synthesize high-resolution OCT volumes with a global-to-local refinement process, amortizing the memory and computational demands.
FRCNet Frequency and Region Consistency for Semi-supervised Medical Image Segmentation
Limited labeled data hinder the application of deep learning in medical domain.
Reliable Source Approximation: Source-Free Unsupervised Domain Adaptation for Vestibular Schwannoma MRI Segmentation
Source-Free Unsupervised Domain Adaptation (SFUDA) has recently become a focus in the medical image domain adaptation, as it only utilizes the source model and does not require annotated target data.
Is Dataset Quality Still a Concern in Diagnosis Using Large Foundation Model?
Furthermore, we discovered that overall fine-tuning is an effective adapter for LFM to mitigate the impact of dataset quality issues.
MM-Retinal: Knowledge-Enhanced Foundational Pretraining with Fundus Image-Text Expertise
Current fundus image analysis models are predominantly built for specific tasks relying on individual datasets.
Topicwise Separable Sentence Retrieval for Medical Report Generation
To ensure comprehensive learning of both common and rare topics, we categorize queries into common and rare types to learn differentiated topics, and then propose Topic Contrastive Loss to effectively align topics and queries in the latent space.
RaffeSDG: Random Frequency Filtering enabled Single-source Domain Generalization for Medical Image Segmentation
To tackle domain shifts in data-scarce medical scenarios, we propose a Random frequency filtering enabled Single-source Domain Generalization algorithm (RaffeSDG), which promises robust out-of-domain inference with segmentation models trained on a single-source domain.
An Aggregation-Free Federated Learning for Tackling Data Heterogeneity
FedAF inherently avoids the issue of client drift, enhances the quality of condensed data amid notable data heterogeneity, and improves the global model performance.
Multimodal Fusion on Low-quality Data: A Comprehensive Survey
Multimodal fusion focuses on integrating information from multiple modalities with the goal of more accurate prediction, which has achieved remarkable progress in a wide range of scenarios, including autonomous driving and medical diagnosis.
MedRG: Medical Report Grounding with Multi-modal Large Language Model
Medical Report Grounding is pivotal in identifying the most relevant regions in medical images based on a given phrase query, a critical aspect in medical image analysis and radiological diagnosis.
Instrument-tissue Interaction Detection Framework for Surgical Video Understanding
To reason relationships between instruments and tissues, a Temporal Graph (TG) Layer is proposed with intra-frame connections to exploit relationships between instruments and tissues in the same frame and inter-frame connections to model the temporal information for the same instance.
Teaching AI the Anatomy Behind the Scan: Addressing Anatomical Flaws in Medical Image Segmentation with Learnable Prior
Imposing key anatomical features, such as the number of organs, their shapes and relative positions, is crucial for building a robust multi-organ segmentation model.
Training-free image style alignment for self-adapting domain shift on handheld ultrasound devices
Moreover, the models trained on standard ultrasound device data are constrained by training data distribution and perform poorly when directly applied to handheld device data.
Dual-scale Enhanced and Cross-generative Consistency Learning for Semi-supervised Medical Image Segmentation
To address scale variation, we present a scale-enhanced consistency constraint, which ensures consistency in the segmentation maps generated from the same input image at different scales.
VSR-Net: Vessel-like Structure Rehabilitation Network with Graph Clustering
Based on this, we propose a novel Vessel-like Structure Rehabilitation Network (VSR-Net) to rehabilitate subsection ruptures and improve the model calibration based on coarse vessel-like structure segmentation results.
Enhancing and Adapting in the Clinic: Source-free Unsupervised Domain Adaptation for Medical Image Enhancement
Additionally, a pseudo-label picker is developed to boost the knowledge distillation of enhancement tasks.
Segment Anything Model-guided Collaborative Learning Network for Scribble-supervised Polyp Segmentation
Specifically, we first propose a Cross-level Enhancement and Aggregation Network (CEA-Net) for weakly-supervised polyp segmentation.
A Survey on Deep Learning for Polyp Segmentation: Techniques, Challenges and Future Trends
This paper provides a comprehensive review of polyp segmentation algorithms.
Resfusion: Denoising Diffusion Probabilistic Models for Image Restoration Based on Prior Residual Noise
We propose Resfusion, a general framework that incorporates the residual term into the diffusion forward process, starting the reverse process directly from the noisy degraded images.
Ranked #2 on
Single Image Deraining
on Raindrop
A Survey of Emerging Applications of Diffusion Probabilistic Models in MRI
Diffusion probabilistic models (DPMs) which employ explicit likelihood characterization and a gradual sampling process to synthesize data, have gained increasing research interest.
Polar-Net: A Clinical-Friendly Model for Alzheimer's Disease Detection in OCTA Images
Optical Coherence Tomography Angiography (OCTA) is a promising tool for detecting Alzheimer's disease (AD) by imaging the retinal microvasculature.
Federated Learning for Clinical Structured Data: A Benchmark Comparison of Engineering and Statistical Approaches
Our goal was to bridge the gap by presenting the first comprehensive comparison of FL frameworks from both engineering and statistical domains.
Adapting Large Language Models for Content Moderation: Pitfalls in Data Engineering and Supervised Fine-tuning
Specifically, we discuss the differences between discriminative and generative models using content moderation as an example.
ACT-Net: Anchor-context Action Detection in Surgery Videos
In this paper, to accurately detect fine-grained actions that happen at every moment, we propose an anchor-context action detection network (ACTNet), including an anchor-context detection (ACD) module and a class conditional diffusion (CCD) module, to answer the following questions: 1) where the actions happen; 2) what actions are; 3) how confidence predictions are.
Shifting More Attention to Breast Lesion Segmentation in Ultrasound Videos
We also devise a localization-based contrastive loss to reduce the lesion location distance between neighboring video frames within the same video and enlarge the location distances between frames from different ultrasound videos.
Ranked #3 on
Video Polyp Segmentation
on SUN-SEG-Easy
Video Adverse-Weather-Component Suppression Network via Weather Messenger and Adversarial Backpropagation
In this work, we propose the first framework for restoring videos from all adverse weather conditions by developing a video adverse-weather-component suppression network (ViWS-Net).
A Spatial-Temporal Deformable Attention based Framework for Breast Lesion Detection in Videos
Existing video-based breast lesion detection approaches typically perform temporal feature aggregation of deep backbone features based on the self-attention operation.
A Generic Fundus Image Enhancement Network Boosted by Frequency Self-supervised Representation Learning
Fundus photography is prone to suffer from image quality degradation that impacts clinical examination performed by ophthalmologists or intelligent systems.
Federated Model Aggregation via Self-Supervised Priors for Highly Imbalanced Medical Image Classification
Existing federated methods under highly imbalanced datasets primarily focus on optimizing a global model without incorporating the intra-class variations that can arise in medical imaging due to different populations, findings, and scanners.
DVPT: Dynamic Visual Prompt Tuning of Large Pre-trained Models for Medical Image Analysis
Firstly, the frozen features are transformed by an lightweight bottleneck layer to learn the domain-specific distribution of downstream medical tasks, and then a few learnable visual prompts are used as dynamic queries and then conduct cross-attention with the transformed features, attempting to acquire sample-specific knowledge that are suitable for each sample.
Frequency-mixed Single-source Domain Generalization for Medical Image Segmentation
Consequently, domain generalization (DG) is developed to boost the performance of segmentation models on unseen domains.
A Multi-view Impartial Decision Network for Frontotemporal Dementia Diagnosis
To achieve this, we employ multiple expert models to extract evidence from the abundant neural network information contained in fMRI images.
SAM-U: Multi-box prompts triggered uncertainty estimation for reliable SAM in medical image
Recently, Segmenting Anything has taken an important step towards general artificial intelligence.
Proceedings of the 40th International Conference on Machine Learning
Specifically, we find that the confidence estimated by current models could even increase when some modalities are corrupted.
A Simple Data Augmentation for Feature Distribution Skewed Federated Learning
In this paper, we focus on the feature distribution skewed FL scenario, a common non-IID situation in real-world applications where data from different clients exhibit varying underlying distributions.
Provable Dynamic Fusion for Low-Quality Multimodal Data
The inherent challenge of multimodal fusion is to precisely capture the cross-modal correlation and flexibly conduct cross-modal interaction.
dugMatting: Decomposed-Uncertainty-Guided Matting
Cutting out an object and estimating its opacity mask, known as image matting, is a key task in image and video editing.
Calibrating Multimodal Learning
Specifically, we find that the confidence estimated by current models could even increase when some modalities are corrupted.
Elongated Physiological Structure Segmentation via Spatial and Scale Uncertainty-aware Network
Second, we extract the uncertainty under different scales and propose the multi-scale uncertainty-aware (MSUA) fusion module to integrate structure contexts from hierarchical predictions, strengthening the final prediction.
PALM: Open Fundus Photograph Dataset with Pathologic Myopia Recognition and Anatomical Structure Annotation
Our databases comprises 1200 images with associated labels for the pathologic myopia category and manual annotations of the optic disc, the position of the fovea and delineations of lesions such as patchy retinal atrophy (including peripapillary atrophy) and retinal detachment.
Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation
In Med-SA, we propose Space-Depth Transpose (SD-Trans) to adapt 2D SAM to 3D medical images and Hyper-Prompting Adapter (HyP-Adpt) to achieve prompt-conditioned adaptation.
Ranked #2 on
Medical Image Segmentation
on Synapse multi-organ CT
Uncertainty-inspired Open Set Learning for Retinal Anomaly Identification
Failure to recognize samples from the classes unseen during training is a major limitation of artificial intelligence in the real-world implementation for recognition and classification of retinal anomalies.
Learning Federated Visual Prompt in Null Space for MRI Reconstruction
Federated Magnetic Resonance Imaging (MRI) reconstruction enables multiple hospitals to collaborate distributedly without aggregating local data, thereby protecting patient privacy.
Federated Uncertainty-Aware Aggregation for Fundus Diabetic Retinopathy Staging
Our TWEU employs an evidential deep layer to produce the uncertainty score with the DR staging results for client reliability evaluation.
Model Barrier: A Compact Un-Transferable Isolation Domain for Model Intellectual Property Protection
As scientific and technological advancements result from human intellectual labor and computational costs, protecting model intellectual property (IP) has become increasingly important to encourage model creators and owners.
DiffMIC: Dual-Guidance Diffusion Network for Medical Image Classification
However, while a substantial amount of diffusion-based research has focused on generative tasks, few studies have applied diffusion models to general medical image classification.
Diff-UNet: A Diffusion Embedded Network for Volumetric Segmentation
Our experimental results also indicate the universality and effectiveness of the proposed model.
Reliable Multimodality Eye Disease Screening via Mixture of Student's t Distributions
To address this issue, we introduce a novel multimodality evidential fusion pipeline for eye disease screening, EyeMoSt, which provides a measure of confidence for unimodality and elegantly integrates the multimodality information from a multi-distribution fusion perspective.
Uncertainty-informed Mutual Learning for Joint Medical Image Classification and Segmentation
Our UML has the potential to explore the development of more reliable and explainable medical image analysis models.
Preventing Unauthorized AI Over-Analysis by Medical Image Adversarial Watermarking
The advancement of deep learning has facilitated the integration of Artificial Intelligence (AI) into clinical practices, particularly in computer-aided diagnosis.
Learning Physical-Spatio-Temporal Features for Video Shadow Removal
Then, we develop a progressive aggregation module to enhance the spatio and temporal characteristics of features maps, and effectively integrate the three kinds of features.
Medical Phrase Grounding with Region-Phrase Context Contrastive Alignment
To enable MedRPG to locate nuanced medical findings with better region-phrase correspondences, we further propose Tri-attention Context contrastive alignment (TaCo).
Bridging Synthetic and Real Images: a Transferable and Multiple Consistency aided Fundus Image Enhancement Framework
Moreover, we also propose a novel multi-stage multi-attention guided enhancement network (MAGE-Net) as the backbones of our teacher and student network.
A Review of Uncertainty Estimation and its Application in Medical Imaging
We further discuss how they can be estimated in medical imaging.
Reliable Federated Disentangling Network for Non-IID Domain Feature
To the best of our knowledge, our proposed RFedDis is the first work to develop an FL approach based on evidential uncertainty combined with feature disentangling, which enhances the performance and reliability of FL in non-IID domain features.
MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer
To effectively integrate these two cutting-edge techniques for the Medical image segmentation, we propose a novel Transformer-based Diffusion framework, called MedSegDiff-V2.
Ranked #3 on
Medical Image Segmentation
on Synapse multi-organ CT
Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST
Lastly, compared to other methods, GraphST’s cell type deconvolution achieved higher accuracy on simulated data and better captured spatial niches such as the germinal centers of the lymph node in experimentally acquired data.
Towards Reliable Medical Image Segmentation by utilizing Evidential Calibrated Uncertainty
DEviS not only enhances the calibration and robustness of baseline segmentation accuracy but also provides high-efficiency uncertainty estimation for reliable predictions.
Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images
Specifically, aiming at improving the model's ability to learn the complex pathological features of retinal edema lesions in OCT images, we develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module of our newly designed.
ExpNet: A unified network for Expert-Level Classification
In the paper, we call them expert-level classification.
Dual Multi-scale Mean Teacher Network for Semi-supervised Infection Segmentation in Chest CT Volume for COVID-19
Automated detecting lung infections from computed tomography (CT) data plays an important role for combating COVID-19.
Degradation-invariant Enhancement of Fundus Images via Pyramid Constraint Network
For boosting the clinical deployment of fundus image enhancement, this paper proposes the pyramid constraint to develop a degradation-invariant enhancement network (PCE-Net), which mitigates the demand for clinical data and stably enhances unknown data.
Localizing Anatomical Landmarks in Ocular Images using Zoom-In Attentive Networks
Therefore, localization has its unique challenges different from segmentation or detection.
Learning to screen Glaucoma like the ophthalmologists
GAMMA Challenge is organized to encourage the AI models to screen the glaucoma from a combination of 2D fundus image and 3D optical coherence tomography volume, like the ophthalmologists.
Unsupervised Domain Adaptation via Style-Aware Self-intermediate Domain
Specifically, we propose a novel learning strategy of SSID, which selects samples from both source and target domains as anchors, and then randomly fuses the object and style features of these anchors to generate labeled and style-rich intermediate auxiliary features for knowledge transfer.
Dataset and Evaluation algorithm design for GOALS Challenge
Glaucoma causes irreversible vision loss due to damage to the optic nerve, and there is no cure for glaucoma. OCT imaging modality is an essential technique for assessing glaucomatous damage since it aids in quantifying fundus structures.
A New Dataset and A Baseline Model for Breast Lesion Detection in Ultrasound Videos
Moreover, we learn video-level features to classify the breast lesions of the original video as benign or malignant lesions to further enhance the final breast lesion detection performance in ultrasound videos.
TBraTS: Trusted Brain Tumor Segmentation
In our method, uncertainty is modeled explicitly using subjective logic theory, which treats the predictions of backbone neural network as subjective opinions by parameterizing the class probabilities of the segmentation as a Dirichlet distribution.
Structure-consistent Restoration Network for Cataract Fundus Image Enhancement
In this paper, to circumvent the strict deployment requirement, a structure-consistent restoration network (SCR-Net) for cataract fundus images is developed from synthesized data that shares an identical structure.
Progressive Multi-scale Consistent Network for Multi-class Fundus Lesion Segmentation
The two proposed PFF and DAB blocks can be integrated with the off-the-shelf backbone networks to address the two issues of multi-scale and feature inconsistency in the multi-class segmentation of fundus lesions, which will produce better feature representation in the feature space.
GCoNet+: A Stronger Group Collaborative Co-Salient Object Detector
In this paper, we present a novel end-to-end group collaborative learning network, termed GCoNet+, which can effectively and efficiently (250 fps) identify co-salient objects in natural scenes.
Ranked #1 on
Co-Salient Object Detection
on CoCA
Structure Unbiased Adversarial Model for Medical Image Segmentation
It consists of a spatial transformation block followed by an intensity distribution rendering module.
Trusted Multi-View Classification with Dynamic Evidential Fusion
With this in mind, we propose a novel multi-view classification algorithm, termed trusted multi-view classification (TMC), providing a new paradigm for multi-view learning by dynamically integrating different views at an evidence level.
Global-and-Local Collaborative Learning for Co-Salient Object Detection
In this paper, we propose a global-and-local collaborative learning architecture, which includes a global correspondence modeling (GCM) and a local correspondence modeling (LCM) to capture comprehensive inter-image corresponding relationship among different images from the global and local perspectives.
RSCFed: Random Sampling Consensus Federated Semi-supervised Learning
In this paper, we present a Random Sampling Consensus Federated learning, namely RSCFed, by considering the uneven reliability among models from fully-labeled clients, fully-unlabeled clients or partially labeled clients.
FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correction
Federated learning (FL) allows multiple clients to collectively train a high-performance global model without sharing their private data.
An Annotation-free Restoration Network for Cataractous Fundus Images
The restoration model is learned from the synthesized images and adapted to real cataract images.
REFUGE2 Challenge: A Treasure Trove for Multi-Dimension Analysis and Evaluation in Glaucoma Screening
Here we release a multi-annotation, multi-quality, and multi-device color fundus image dataset for glaucoma analysis on an original challenge -- Retinal Fundus Glaucoma Challenge 2nd Edition (REFUGE2).
ADAM Challenge: Detecting Age-related Macular Degeneration from Fundus Images
The ADAM challenge consisted of four tasks which cover the main aspects of detecting and characterizing AMD from fundus images, including detection of AMD, detection and segmentation of optic disc, localization of fovea, and detection and segmentation of lesions.
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.
Consistency and Diversity induced Human Motion Segmentation
Besides, a novel constraint based on the Hilbert Schmidt Independence Criterion (HSIC) is introduced to ensure the diversity of multi-level subspace representations, which enables the complementarity of multi-level representations to be explored to boost the transfer learning performance.
Transformers in Medical Imaging: A Survey
Following unprecedented success on the natural language tasks, Transformers have been successfully applied to several computer vision problems, achieving state-of-the-art results and prompting researchers to reconsider the supremacy of convolutional neural networks (CNNs) as {de facto} operators.
Boosting RGB-D Saliency Detection by Leveraging Unlabeled RGB Images
The depth estimation branch is trained with RGB-D images and then used to estimate the pseudo depth maps for all unlabeled RGB images to form the paired data.
Specificity-Preserving Federated Learning for MR Image Reconstruction
The core idea is to divide the MR reconstruction model into two parts: a globally shared encoder to obtain a generalized representation at the global level, and a client-specific decoder to preserve the domain-specific properties of each client, which is important for collaborative reconstruction when the clients have unique distribution.
Trustworthy Long-Tailed Classification
To address these issues, we propose a Trustworthy Long-tailed Classification (TLC) method to jointly conduct classification and uncertainty estimation to identify hard samples in a multi-expert framework.
Ranked #21 on
Long-tail Learning
on CIFAR-10-LT (ρ=100)
Trustworthy Multimodal Regression with Mixture of Normal-inverse Gamma Distributions
Multimodal regression is a fundamental task, which integrates the information from different sources to improve the performance of follow-up applications.
Deep multi-modal aggregation network for MR image reconstruction with auxiliary modality
Magnetic resonance (MR) imaging produces detailed images of organs and tissues with better contrast, but it suffers from a long acquisition time, which makes the image quality vulnerable to say motion artifacts.
Proxy-bridged Image Reconstruction Network for Anomaly Detection in Medical Images
To mitigate this problem, in this paper, we propose a novel Proxy-bridged Image Reconstruction Network (ProxyAno) for anomaly detection in medical images.
Exploring Separable Attention for Multi-Contrast MR Image Super-Resolution
Our SANet could explore the areas of high-intensity and low-intensity regions in the "forward" and "reverse" directions with the help of the auxiliary contrast, while learning clearer anatomical structure and edge information for the SR of a target-contrast MR image.
VIL-100: A New Dataset and A Baseline Model for Video Instance Lane Detection
Lane detection plays a key role in autonomous driving.
Specificity-preserving RGB-D Saliency Detection
To effectively fuse cross-modal features in the shared learning network, we propose a cross-enhanced integration module (CIM) and then propagate the fused feature to the next layer for integrating cross-level information.
Ranked #2 on
Object Detection
on PKU-DDD17-Car
Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers
Unlike existing CNN-based methods, we adopt a transformer encoder, which learns more powerful and robust representations.
Ranked #11 on
Medical Image Segmentation
on CVC-ColonDB
From Synthetic to Real: Image Dehazing Collaborating with Unlabeled Real Data
Our DID-Net predicts the three component maps by progressively integrating features across scales, and refines each map by passing an independent refinement network.
Ranked #8 on
Image Dehazing
on Haze4k
Few-Shot Domain Adaptation with Polymorphic Transformers
Thus, the chance of overfitting the annotations is greatly reduced, and the model can perform robustly on the target domain after being trained on a few annotated images.
Multi-Modal Transformer for Accelerated MR Imaging
To this end, we propose a multi-modal transformer (MTrans), which is capable of transferring multi-scale features from the target modality to the auxiliary modality, for accelerated MR imaging.
Federated Noisy Client Learning
Federated learning (FL) collaboratively trains a shared global model depending on multiple local clients, while keeping the training data decentralized in order to preserve data privacy.
Task Transformer Network for Joint MRI Reconstruction and Super-Resolution
Then, a task transformer module is designed to embed and synthesize the relevance between the two tasks.
Ranked #9 on
Image Super-Resolution
on IXI
A Multi-Branch Hybrid Transformer Networkfor Corneal Endothelial Cell Segmentation
Corneal endothelial cell segmentation plays a vital role inquantifying clinical indicators such as cell density, coefficient of variation, and hexagonality.
Multi-Contrast MRI Super-Resolution via a Multi-Stage Integration Network
In this work, we propose a multi-stage integration network (i. e., MINet) for multi-contrast MRI SR, which explicitly models the dependencies between multi-contrast images at different stages to guide image SR.
Combating Ambiguity for Hash-code Learning in Medical Instance Retrieval
When encountering a dubious diagnostic case, medical instance retrieval can help radiologists make evidence-based diagnoses by finding images containing instances similar to a query case from a large image database.
Progressively Normalized Self-Attention Network for Video Polyp Segmentation
Existing video polyp segmentation (VPS) models typically employ convolutional neural networks (CNNs) to extract features.
Ranked #10 on
Video Polyp Segmentation
on SUN-SEG-Easy (Unseen)
DONet: Dual-Octave Network for Fast MR Image Reconstruction
In this paper, we propose the Dual-Octave Network (DONet), which is capable of learning multi-scale spatial-frequency features from both the real and imaginary components of MR data, for fast parallel MR image reconstruction.
VideoLT: Large-scale Long-tailed Video Recognition
In this paper, we introduce VideoLT, a large-scale long-tailed video recognition dataset, as a step toward real-world video recognition.
Global Guidance Network for Breast Lesion Segmentation in Ultrasound Images
The BD modules learn additional breast lesion boundary map to enhance the boundary quality of a segmentation result refinement.
Group Collaborative Learning for Co-Salient Object Detection
We present a novel group collaborative learning framework (GCoNet) capable of detecting co-salient objects in real time (16ms), by simultaneously mining consensus representations at group level based on the two necessary criteria: 1) intra-group compactness to better formulate the consistency among co-salient objects by capturing their inherent shared attributes using our novel group affinity module; 2) inter-group separability to effectively suppress the influence of noisy objects on the output by introducing our new group collaborating module conditioning the inconsistent consensus.
Ranked #5 on
Co-Salient Object Detection
on CoCA
Triple-cooperative Video Shadow Detection
The bottleneck is the lack of a well-established dataset with high-quality annotations for video shadow detection.
Trusted Multi-View Classification
To this end, we propose a novel multi-view classification method, termed trusted multi-view classification, which provides a new paradigm for multi-view learning by dynamically integrating different views at an evidence level.
Deep Triplet Hashing Network for Case-based Medical Image Retrieval
The triplet cross-entropy loss can help to map the classification information of images and similarity between images into the hash codes.
Applications of Deep Learning in Fundus Images: A Review
The use of fundus images for the early screening of eye diseases is of great clinical importance.
Multi-View Disentangled Representation
Learning effective representations for data with multiple views is crucial in machine learning and pattern recognition.
Visual-Textual Attentive Semantic Consistency for Medical Report Generation
Second, the progressive report decoder consists of a sentence decoder and a word decoder, where we propose image-sentence matching and description accuracy losses to constrain the visual-textual semantic consistency.
Colonoscopy Polyp Detection: Domain Adaptation From Medical Report Images to Real-time Videos
However, there are several issues between the image-based training and video-based inference, including domain differences, lack of positive samples, and temporal smoothness.
Deep Partial Multi-View Learning
Although multi-view learning has made signifificant progress over the past few decades, it is still challenging due to the diffificulty in modeling complex correlations among different views, especially under the context of view missing.
CS2-Net: Deep Learning Segmentation of Curvilinear Structures in Medical Imaging
Automated detection of curvilinear structures, e. g., blood vessels or nerve fibres, from medical and biomedical images is a crucial early step in automatic image interpretation associated to the management of many diseases.
M3Lung-Sys: A Deep Learning System for Multi-Class Lung Pneumonia Screening from CT Imaging
To counter the outbreak of COVID-19, the accurate diagnosis of suspected cases plays a crucial role in timely quarantine, medical treatment, and preventing the spread of the pandemic.
Can You Spot the Chameleon? Adversarially Camouflaging Images from Co-Salient Object Detection
In this paper, we address this problem from the perspective of adversarial attacks and identify a novel task: adversarial co-saliency attack.
Adversarial Exposure Attack on Diabetic Retinopathy Imagery Grading
Diabetic Retinopathy (DR) is a leading cause of vision loss around the world.
NuI-Go: Recursive Non-Local Encoder-Decoder Network for Retinal Image Non-Uniform Illumination Removal
We further demonstrate the advantages of the proposed method for improving the accuracy of retinal vessel segmentation.
Retinal Image Segmentation with a Structure-Texture Demixing Network
Retinal image segmentation plays an important role in automatic disease diagnosis.
ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model
To address these issues, for the first time in the field of retinal image analysis we construct a dedicated Retinal OCT-A SEgmentation dataset (ROSE), which consists of 229 OCT-A images with vessel annotations at either centerline-level or pixel level.
Ranked #1 on
Retinal Vessel Segmentation
on ROSE-1 DVC
Re-thinking Co-Salient Object Detection
CoSOD is an emerging and rapidly growing extension of salient object detection (SOD), which aims to detect the co-occurring salient objects in a group of images.
Ranked #7 on
Co-Salient Object Detection
on CoCA
PraNet: Parallel Reverse Attention Network for Polyp Segmentation
To address these challenges, we propose a parallel reverse attention network (PraNet) for accurate polyp segmentation in colonoscopy images.
Ranked #3 on
Camouflaged Object Segmentation
on PCOD_1200
Reconstruction and Quantification of 3D Iris Surface for Angle-Closure Glaucoma Detection in Anterior Segment OCT
We consider it to be the first work to detect angle-closure glaucoma by means of 3D representation.
Open-Narrow-Synechiae Anterior Chamber Angle Classification in AS-OCT Sequences
However, clinical diagnosis requires a more discriminating ACA three-class system (i. e., open, narrow, or synechiae angles) for the benefit of clinicians who seek better to understand the progression of the spectrum of angle-closure glaucoma types.
M2Net: Multi-modal Multi-channel Network for Overall Survival Time Prediction of Brain Tumor Patients
Then, we use a modality-specific network to extract implicit and high-level features from different MR scans.
Modeling and Enhancing Low-quality Retinal Fundus Images
Retinal fundus images are widely used for the clinical screening and diagnosis of eye diseases.
AGE Challenge: Angle Closure Glaucoma Evaluation in Anterior Segment Optical Coherence Tomography
To address this, we organized the Angle closure Glaucoma Evaluation challenge (AGE), held in conjunction with MICCAI 2019.
Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images
Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing the world to face an existential health crisis.
A Thorough Comparison Study on Adversarial Attacks and Defenses for Common Thorax Disease Classification in Chest X-rays
In this paper, we aim to review various adversarial attack and defense methods on chest X-rays.
Hi-Net: Hybrid-fusion Network for Multi-modal MR Image Synthesis
Medical image synthesis has been proposed as an effective solution to this, where any missing modalities are synthesized from the existing ones.
CPM-Nets: Cross Partial Multi-View Networks
Despite multi-view learning progressed fast in past decades, it is still challenging due to the difficulty in modeling complex correlation among different views, especially under the context of view missing.
Sparse-GAN: Sparsity-constrained Generative Adversarial Network for Anomaly Detection in Retinal OCT Image
With the development of convolutional neural network, deep learning has shown its success for retinal disease detection from optical coherence tomography (OCT) images.
REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs
As part of REFUGE, we have publicly released a data set of 1200 fundus images with ground truth segmentations and clinical glaucoma labels, currently the largest existing one.
SkrGAN: Sketching-rendering Unconditional Generative Adversarial Networks for Medical Image Synthesis
Generative Adversarial Networks (GANs) have the capability of synthesizing images, which have been successfully applied to medical image synthesis tasks.
Attention Guided Network for Retinal Image Segmentation
Learning structural information is critical for producing an ideal result in retinal image segmentation.
ET-Net: A Generic Edge-aTtention Guidance Network for Medical Image Segmentation
Segmentation is a fundamental task in medical image analysis.
Ranked #1 on
Optic Disc Segmentation
on REFUGE
Evaluation of Retinal Image Quality Assessment Networks in Different Color-spaces
Retinal image quality assessment (RIQA) is essential for controlling the quality of retinal imaging and guaranteeing the reliability of diagnoses by ophthalmologists or automated analysis systems.
CE-Net: Context Encoder Network for 2D Medical Image Segmentation
In this paper, we propose a context encoder network (referred to as CE-Net) to capture more high-level information and preserve spatial information for 2D medical image segmentation.
Ranked #1 on
Optic Disc Segmentation
on Messidor
Angle-Closure Detection in Anterior Segment OCT based on Multi-Level Deep Network
A Multi-Level Deep Network (MLDN) is proposed to formulate this learning, which utilizes three particular AS-OCT regions based on clinical priors: the global anterior segment structure, local iris region, and anterior chamber angle (ACA) patch.
HSCS: Hierarchical Sparsity Based Co-saliency Detection for RGBD Images
In this paper, we propose a novel co-saliency detection method for RGBD images based on hierarchical sparsity reconstruction and energy function refinement.
Multi-Context Deep Network for Angle-Closure Glaucoma Screening in Anterior Segment OCT
A major cause of irreversible visual impairment is angle-closure glaucoma, which can be screened through imagery from Anterior Segment Optical Coherence Tomography (AS-OCT).
Person Re-Identification by Semantic Region Representation and Topology Constraint
In this paper, we propose a novel person re-identification method, which consists of a reliable representation called Semantic Region Representation (SRR), and an effective metric learning with Mapping Space Topology Constraint (MSTC).
Disc-aware Ensemble Network for Glaucoma Screening from Fundus Image
Specifically, a novel Disc-aware Ensemble Network (DENet) for automatic glaucoma screening is proposed, which integrates the deep hierarchical context of the global fundus image and the local optic disc region.
Structure-preserving Guided Retinal Image Filtering and Its Application for Optic Disc Analysis
It often obscures the details in the retinal images and posts challenges in retinal image processing and analysing tasks.
A Cascaded Convolutional Neural Network for Single Image Dehazing
Different from previous learning-based methods, we propose a flexible cascaded CNN for single hazy image restoration, which considers the medium transmission and global atmospheric light jointly by two task-driven subnetworks.
Review of Visual Saliency Detection with Comprehensive Information
With the acquisition technology development, more comprehensive information, such as depth cue, inter-image correspondence, or temporal relationship, is available to extend image saliency detection to RGBD saliency detection, co-saliency detection, or video saliency detection.
Joint Optic Disc and Cup Segmentation Based on Multi-label Deep Network and Polar Transformation
The proposed M-Net mainly consists of multi-scale input layer, U-shape convolutional network, side-output layer, and multi-label loss function.
Ranked #4 on
Optic Disc Segmentation
on REFUGE
An Iterative Co-Saliency Framework for RGBD Images
In this paper, we propose an iterative RGBD co-saliency framework, which utilizes the existing single saliency maps as the initialization, and generates the final RGBD cosaliency map by using a refinement-cycle model.
Co-saliency Detection for RGBD Images Based on Multi-constraint Feature Matching and Cross Label Propagation
Different from the most existing co-saliency methods focusing on RGB images, this paper proposes a novel co-saliency detection model for RGBD images, which utilizes the depth information to enhance identification of co-saliency.
Latent Multi-View Subspace Clustering
In this paper, we propose a novel Latent Multi-view Subspace Clustering (LMSC) method, which clusters data points with latent representation and simultaneously explores underlying complementary information from multiple views.
A Review of Co-saliency Detection Technique: Fundamentals, Applications, and Challenges
Co-saliency detection is a newly emerging and rapidly growing research area in computer vision community.
Low-Rank Tensor Constrained Multiview Subspace Clustering
We introduce a low-rank tensor constraint to explore the complementary information from multiple views and, accordingly, establish a novel method called Low-rank Tensor constrained Multiview Subspace Clustering (LT-MSC).
Diversity-Induced Multi-View Subspace Clustering
In this paper, we focus on how to boost the multi-view clustering by exploring the complementary information among multi-view features.
Object-Based RGBD Image Co-Segmentation With Mutex Constraint
We present an object-based co-segmentation method that takes advantage of depth data and is able to correctly handle noisy images in which the common foreground object is missing.
Object-based Multiple Foreground Video Co-segmentation
We present a video co-segmentation method that uses category-independent object proposals as its basic element and can extract multiple foreground objects in a video set.
