Search Results for author:
Found 90 papers, 34 papers with code
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.
A Low-Dimensional Step Pattern Analysis Algorithm With Application to Multimodal Retinal Image Registration
These customized step patterns are robust to non-linear intensity changes, which are well-suited for multimodal retinal image registration.
A novel learning-based frame pooling method for Event Detection
Detecting complex events in a large video collection crawled from video websites is a challenging task.
DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density Estimation
DecideNet starts with estimating the crowd density by generating detection and regression based density maps separately.
Ranked #10 on
Crowd Counting
on WorldExpo’10
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
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.
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.
Multi-Cell Multi-Task Convolutional Neural Networks for Diabetic Retinopathy Grading
To considering the relationships of images with different stages, we propose a \textbf{Multi-Task} learning strategy which predicts the label with both classification and regression.
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).
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.
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
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.
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.
The Channel Attention based Context Encoder Network for Inner Limiting Membrane Detection
The optic disc segmentation is an important step for retinal image-based disease diagnosis such as glaucoma.
GetNet: Get Target Area for Image Pairing
Image pairing is an important research task in the field of computer vision.
Dense Dilated Network with Probability Regularized Walk for Vessel Detection
Many methods have been proposed for vessel detection.
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.
BioNet: Infusing Biomarker Prior into Global-to-Local Network for Choroid Segmentation in Optical Coherence Tomography Images
Firstly, our method trains a biomarker prediction network to learn the features of the biomarker.
A Two-Stream Meticulous Processing Network for Retinal Vessel Segmentation
In this paper, we propose a novel two-stream Meticulous-Processing Network (MP-Net) for tackling this problem.
Lossless Attention in Convolutional Networks for Facial Expression Recognition in the Wild
And we validate our method on the Aff-Wild2 datasets released by the Challenge.
Facial Expression Recognition
Facial Expression Recognition (FER)
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.
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.
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.
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
Encoding Structure-Texture Relation with P-Net for Anomaly Detection in Retinal Images
In the end, we further utilize the reconstructed image to extract the structure and measure the difference between structure extracted from original and the reconstructed image.
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.
Probabilistic Latent Factor Model for Collaborative Filtering with Bayesian Inference
Latent Factor Model (LFM) is one of the most successful methods for Collaborative filtering (CF) in the recommendation system, in which both users and items are projected into a joint latent factor space.
Attention-based Saliency Hashing for Ophthalmic Image Retrieval
The different grades or classes of ophthalmic images may be share similar overall performance but have subtle differences that can be differentiated by mining salient regions.
Machine Learning for Cataract Classification and Grading on Ophthalmic Imaging Modalities: A Survey
This survey provides a comprehensive survey of recent advances in machine learning techniques for cataract classification/grading based on ophthalmic images.
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.
3D Vessel Reconstruction in OCT-Angiography via Depth Map Estimation
Experimental results demonstrate that our method is effective in the depth prediction and 3D vessel reconstruction for OCTA images.% results may be used to guide subsequent vascular analysis
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.
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.
Weighing Features of Lung and Heart Regions for Thoracic Disease Classification
By zeroing features of non-lung and heart regions in attention maps, we can effectively exploit their disease-specific cues in lung and heart regions.
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.
Spatial-context-aware deep neural network for multi-class image classification
Multi-label image classification is a fundamental but challenging task in computer vision.
Segment and Complete: Defending Object Detectors against Adversarial Patch Attacks with Robust Patch Detection
In addition, we design a robust shape completion algorithm, which is guaranteed to remove the entire patch from the images if the outputs of the patch segmenter are within a certain Hamming distance of the ground-truth patch masks.
Mutual Adversarial Training: Learning together is better than going alone
In other words, we can make a weak model more robust with the help of a strong teacher model.
Interpolated Joint Space Adversarial Training for Robust and Generalizable Defenses
Under JSTM, we develop novel adversarial attacks and defenses.
Unified Named Entity Recognition as Word-Word Relation Classification
So far, named entity recognition (NER) has been involved with three major types, including flat, overlapped (aka.
Ranked #2 on
Chinese Named Entity Recognition
on OntoNotes 4
SS3D: Sparsely-Supervised 3D Object Detection From Point Cloud
In the meantime, we design a reliable background mining module and a point cloud filling data augmentation strategy to generate the confident data for iteratively learning with reliable supervision.
Deep Learning for Computational Cytology: A Survey
Computational cytology is a critical, rapid-developing, yet challenging topic in the field of medical image computing which analyzes the digitized cytology image by computer-aided technologies for cancer screening.
An Annotation-free Restoration Network for Cataractous Fundus Images
The restoration model is learned from the synthesized images and adapted to real cataract images.
One Model to Synthesize Them All: Multi-contrast Multi-scale Transformer for Missing Data Imputation
In this work, we formulate missing data imputation as a sequence-to-sequence learning problem and propose a multi-contrast multi-scale Transformer (MMT), which can take any subset of input contrasts and synthesize those that are missing.
Structure Unbiased Adversarial Model for Medical Image Segmentation
It consists of a spatial transformation block followed by an intensity distribution rendering module.
Siamese Encoder-based Spatial-Temporal Mixer for Growth Trend Prediction of Lung Nodules on CT Scans
In the management of lung nodules, we are desirable to predict nodule evolution in terms of its diameter variation on Computed Tomography (CT) scans and then provide a follow-up recommendation according to the predicted result of the growing trend of the nodule.
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.
Weighted Concordance Index Loss-based Multimodal Survival Modeling for Radiation Encephalopathy Assessment in Nasopharyngeal Carcinoma Radiotherapy
We design a deep multimodal survival network (MSN) with two feature extractors to learn discriminative features from multimodal data.
SuperVessel: Segmenting High-resolution Vessel from Low-resolution Retinal Image
Vascular segmentation extracts blood vessels from images and serves as the basis for diagnosing various diseases, like ophthalmic diseases.
Retinal Structure Detection in OCTA Image via Voting-based Multi-task Learning
Automated detection of retinal structures, such as retinal vessels (RV), the foveal avascular zone (FAZ), and retinal vascular junctions (RVJ), are of great importance for understanding diseases of the eye and clinical decision-making.
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.
TOE: A Grid-Tagging Discontinuous NER Model Enhanced by Embedding Tag/Word Relations and More Fine-Grained Tags
Concretely, we construct tag representations and embed them into TREM, so that TREM can treat tag and word representations as queries/keys/values and utilize self-attention to model their relationships.
Hard Exudate Segmentation Supplemented by Super-Resolution with Multi-scale Attention Fusion Module
Specifically, we propose a fusion module named Multi-scale Attention Fusion (MAF) module for our dual-stream framework to effectively integrate features of the two tasks.
PolyFormer: Referring Image Segmentation as Sequential Polygon Generation
In this work, instead of directly predicting the pixel-level segmentation masks, the problem of referring image segmentation is formulated as sequential polygon generation, and the predicted polygons can be later converted into segmentation masks.
Ranked #1 on
Referring Expression Segmentation
on ReferIt
(using extra training data)
PPCR: Learning Pyramid Pixel Context Recalibration Module for Medical Image Classification
Spatial attention mechanism has been widely incorporated into deep convolutional neural networks (CNNs) via long-range dependency capturing, significantly lifting the performance in computer vision, but it may perform poorly in medical imaging.
Eye tracking guided deep multiple instance learning with dual cross-attention for fundus disease detection
Concretely, the HITL CAD system was implemented on the multiple instance learning (MIL), where eye-tracking gaze maps were beneficial to cherry-pick diagnosis-related instances.
Learnable Ophthalmology SAM
Based on Segment Anything (SAM), we propose a simple but effective learnable prompt layer suitable for multiple target segmentation in ophthalmology multi-modal images, named Learnable Ophthalmology Segment Anything (SAM).
Attribute-Guided Encryption with Facial Texture Masking
In this paper, we propose Attribute Guided Encryption with Facial Texture Masking (AGE-FTM) that performs a dual manifold adversarial attack on FR systems to achieve both good visual quality and high black box attack success rates.
DiffProtect: Generate Adversarial Examples with Diffusion Models for Facial Privacy Protection
In this work, we ask: can diffusion models be used to generate adversarial examples to improve both visual quality and attack performance?
Mask Attack Detection Using Vascular-weighted Motion-robust rPPG Signals
Detecting 3D mask attacks to a face recognition system is challenging.
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.
TKDP: Threefold Knowledge-enriched Deep Prompt Tuning for Few-shot Named Entity Recognition
Few-shot named entity recognition (NER) exploits limited annotated instances to identify named mentions.
ORGAN: Observation-Guided Radiology Report Generation via Tree Reasoning
This paper explores the task of radiology report generation, which aims at generating free-text descriptions for a set of radiographs.
Label-noise-tolerant medical image classification via self-attention and self-supervised learning
Therefore, in this work, we innovatively devise noise-robust training approach to mitigate the adverse effects of noisy labels in medical image classification.
Cloud Ensemble Learning for Fault Diagnosis of Rolling Bearings with Stochastic Configuration Networks
Concretely, a cloud feature extraction method is first developed by using a backward cloud generator of normal cloud model to mine the uncertainty of fault information.
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.
Constructive Incremental Learning for Fault Diagnosis of Rolling Bearings with Ensemble Domain Adaptation
Given the prevalence of rolling bearing fault diagnosis as a practical issue across various working conditions, the limited availability of samples compounds the challenge.
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.
Promoting Open-domain Dialogue Generation through Learning Pattern Information between Contexts and Responses
In this paper, we first build an open-domain dialogue model based on the pre-trained language model (i. e., GPT-2).
Reset Controller Synthesis by Reach-avoid Analysis for Delay Hybrid Systems
A reset controller plays a crucial role in designing hybrid systems.
Correct-by-Construction for Hybrid Systems by Synthesizing Reset Controller
Controller synthesis, including reset controller, feedback controller, and switching logic controller, provides an essential mechanism to guarantee the correctness and reliability of hybrid systems in a correct-by-construction manner.
Efficient Pyramid Channel Attention Network for Pathological Myopia Recognition
Moreover, motivated by the recent pretraining-and-finetuning paradigm, we attempt to adapt pre-trained natural image models for PM recognition by freezing them and treating the EPCA and other attention modules as adapters.
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.
RECAP: Towards Precise Radiology Report Generation via Dynamic Disease Progression Reasoning
It then combines the historical records, spatiotemporal information, and radiographs for report generation, where a disease progression graph and dynamic progression reasoning mechanism are devised to accurately select the attributes of each observation and progression.
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.
GlanceSeg: Real-time microaneurysm lesion segmentation with gaze-map-guided foundation model for early detection of diabetic retinopathy
In this work, we propose a human-in-the-loop, label-free early DR diagnosis framework called GlanceSeg, based on SAM.
AutoML for Large Capacity Modeling of Meta's Ranking Systems
The large scale of models and tight production schedule requires AutoML to outperform human baselines by only using a small number of model evaluation trials (around 100).
Rankitect: Ranking Architecture Search Battling World-class Engineers at Meta Scale
In industry system, such as ranking system in Meta, it is unclear whether NAS algorithms from the literature can outperform production baselines because of: (1) scale - Meta ranking systems serve billions of users, (2) strong baselines - the baselines are production models optimized by hundreds to thousands of world-class engineers for years since the rise of deep learning, (3) dynamic baselines - engineers may have established new and stronger baselines during NAS search, and (4) efficiency - the search pipeline must yield results quickly in alignment with the productionization life cycle.
DIFFNAT: Improving Diffusion Image Quality Using Natural Image Statistics
Diffusion models have advanced generative AI significantly in terms of editing and creating naturalistic images.
Instruct2Attack: Language-Guided Semantic Adversarial Attacks
We propose Instruct2Attack (I2A), a language-guided semantic attack that generates semantically meaningful perturbations according to free-form language instructions.
GaitContour: Efficient Gait Recognition based on a Contour-Pose Representation
Gait recognition holds the promise to robustly identify subjects based on walking patterns instead of appearance information.
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.
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.
Scale Optimization Using Evolutionary Reinforcement Learning for Object Detection on Drone Imagery
A set of rewards measuring the localization accuracy, the accuracy of predicted labels, and the scale consistency among nearby patches are designed in the agent to guide the scale optimization.
ICON: Improving Inter-Report Consistency of Radiology Report Generation via Lesion-aware Mix-up Augmentation
Previous research on radiology report generation has made significant progress in terms of increasing the clinical accuracy of generated reports.
Flattening Singular Values of Factorized Convolution for Medical Images
Convolutional neural networks (CNNs) have long been the paradigm of choice for robust medical image processing (MIP).
Are Dense Labels Always Necessary for 3D Object Detection from Point Cloud?
Current state-of-the-art (SOTA) 3D object detection methods often require a large amount of 3D bounding box annotations for training.
HyperLLaVA: Dynamic Visual and Language Expert Tuning for Multimodal Large Language Models
Recent advancements indicate that scaling up Multimodal Large Language Models (MLLMs) effectively enhances performance on downstream multimodal tasks.
Ranked #72 on
Visual Question Answering
on MM-Vet
Medical Image Registration and Its Application in Retinal Images: A Review
Medical image registration is vital for disease diagnosis and treatment with its ability to merge diverse information of images, which may be captured under different times, angles, or modalities.
LASER: Tuning-Free LLM-Driven Attention Control for Efficient Text-conditioned Image-to-Animation
Revolutionary advancements in text-to-image models have unlocked new dimensions for sophisticated content creation, e. g., text-conditioned image editing, allowing us to edit the diverse images that convey highly complex visual concepts according to the textual guidance.
