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Found 55 papers, 21 papers with code
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.
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)
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.
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.
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.
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.
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.
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.
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.
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 Unbiased Adversarial Model for Medical Image Segmentation
It consists of a spatial transformation block followed by an intensity distribution rendering module.
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.
An Annotation-free Restoration Network for Cataractous Fundus Images
The restoration model is learned from the synthesized images and adapted to real cataract images.
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.
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.
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
Interpolated Joint Space Adversarial Training for Robust and Generalizable Defenses
Under JSTM, we develop novel adversarial attacks and defenses.
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.
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.
Spatial-context-aware deep neural network for multi-class image classification
Multi-label image classification is a fundamental but challenging task in computer vision.
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.
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.
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.
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.
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
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.
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.
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.
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.
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.
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.
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
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.
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.
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.
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.
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.
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.
Dense Dilated Network with Probability Regularized Walk for Vessel Detection
Many methods have been proposed for vessel detection.
GetNet: Get Target Area for Image Pairing
Image pairing is an important research task in the field of computer vision.
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.
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.
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.
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).
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.
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.
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
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
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.
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.
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.
