Search Results for author:
Found 146 papers, 38 papers with code
Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models
This necessitates a new branch of research focusing on the parameter-efficient adaptation of PLMs, dubbed as delta tuning in this paper.
Diff-UNet: A Diffusion Embedded Network for Volumetric Segmentation
Our experimental results also indicate the universality and effectiveness of the proposed model.
Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation
In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution.
Swin Transformer for Fast MRI
The IM and OM were 2D convolutional layers and the FEM was composed of a cascaded of residual Swin transformer blocks (RSTBs) and 2D convolutional layers.
CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction
However, a limitation of CMR is its slow imaging speed, which causes patient discomfort and introduces artifacts in the images.
MIPI 2022 Challenge on Quad-Bayer Re-mosaic: Dataset and Report
A detailed description of all models developed in this challenge is provided in this paper.
MIPI 2022 Challenge on RGBW Sensor Fusion: Dataset and Report
A detailed description of all models developed in this challenge is provided in this paper.
MIPI 2022 Challenge on RGBW Sensor Re-mosaic: Dataset and Report
A detailed description of all models developed in this challenge is provided in this paper.
ChatAgri: Exploring Potentials of ChatGPT on Cross-linguistic Agricultural Text Classification
In the era of sustainable smart agriculture, a massive amount of agricultural news text is being posted on the Internet, in which massive agricultural knowledge has been accumulated.
Adversarial and Perceptual Refinement for Compressed Sensing MRI Reconstruction
In addition, we introduce a semantic interpretability score, measuring the visibility of the region of interest in both ground truth and reconstructed images, which allows us to objectively quantify the usefulness of the image quality for image post-processing and analysis.
Swin Deformable Attention U-Net Transformer (SDAUT) for Explainable Fast MRI
The main obstacle is the computational cost of the self-attention layer, which is the core part of the Transformer, can be expensive for high resolution MRI inputs.
Lesion Focused Super-Resolution
Super-resolution (SR) for image enhancement has great importance in medical image applications.
How Can We Make GAN Perform Better in Single Medical Image Super-Resolution? A Lesion Focused Multi-Scale Approach
Single image super-resolution (SISR) is of great importance as a low-level computer vision task.
MIASSR: An Approach for Medical Image Arbitrary Scale Super-Resolution
We also employ transfer learning to enable MIASSR to tackle SR tasks of new medical modalities, such as cardiac MR images (ACDC) and chest computed tomography images (COVID-CT).
Multi-Modal Experience Inspired AI Creation
To model such human capabilities, in this paper, we define and solve a novel AI creation problem based on human experiences.
A residual dense vision transformer for medical image super-resolution with segmentation-based perceptual loss fine-tuning
On the other hand, the segmentation-based perceptual loss increases $+0. 14$ dB PSNR on average for SOTA methods, including CNNs and vision transformers.
Heterogeneous Directed Hypergraph Neural Network over abstract syntax tree (AST) for Code Classification
In this study, we propose to represent AST as a heterogeneous directed hypergraph (HDHG) and process the graph by heterogeneous directed hypergraph neural network (HDHGN) for code classification.
Fast MRI Reconstruction: How Powerful Transformers Are?
In particular, a generative adversarial network (GAN) based Swin transformer (ST-GAN) was introduced for the fast MRI reconstruction.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
Adaptive Hierarchical Dual Consistency for Semi-Supervised Left Atrium Segmentation on Cross-Domain Data
For the inter-domain learning, a consistency constraint is applied to the LAs modelled by two dual-modelling networks to exploit the complementary knowledge among different data domains.
CDiffMR: Can We Replace the Gaussian Noise with K-Space Undersampling for Fast MRI?
Different from conventional diffusion models, the degradation operation of our CDiffMR is based on \textit{k}-space undersampling instead of adding Gaussian noise, and the restoration network is trained to harness a de-aliaseing function.
A Multi-scale Information Integration Framework for Infrared and Visible Image Fusion
In this study, we propose a multi-scale dual attention (MDA) framework for infrared and visible image fusion, which is designed to measure and integrate complementary information in both structure and loss function at the image and patch level.
DRAG: Dynamic Region-Aware GCN for Privacy-Leaking Image Detection
However, these methods have two limitations: 1) they neglect other important elements like scenes, textures, and objects beyond the capacity of pretrained object detectors; 2) the correlation among objects is fixed, but a fixed correlation is not appropriate for all the images.
Is Autoencoder Truly Applicable for 3D CT Super-Resolution?
Our work is the first comparative study investigating the suitability of AE architecture for 3D CT SISR tasks and brings a rationale for researchers to re-think the choice of model architectures especially for 3D CT SISR tasks.
Reducing Spatial Fitting Error in Distillation of Denoising Diffusion Models
Based on our analysis with bias-variance decomposition and experimental observations, we attribute the degradation to the spatial fitting error occurring in the training of both the teacher and student model.
Transfer Learning Enhanced Generative Adversarial Networks for Multi-Channel MRI Reconstruction
The transfer learning results for the knee and liver were superior to those of the PI-GAN model trained with its own dataset using a smaller number of training cases.
SegmentAnything helps microscopy images based automatic and quantitative organoid detection and analysis
Organoids are self-organized 3D cell clusters that closely mimic the architecture and function of in vivo tissues and organs.
A Dual Domain Multi-exposure Image Fusion Network based on the Spatial-Frequency Integration
Specifically, we introduce a Spatial-Frequency Fusion Block to facilitate efficient interaction between dual domains and capture complementary information from input images with different exposures.
HDL: Hybrid Deep Learning for the Synthesis of Myocardial Velocity Maps in Digital Twins for Cardiac Analysis
A core part of digital healthcare twins is model-based data synthesis, which permits the generation of realistic medical signals without requiring to cope with the modelling complexity of anatomical and biochemical phenomena producing them in reality.
One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstruction
We demonstrate that training DL models on synthetic data, coupled with enhanced learning techniques, yields in vivo MRI reconstructions comparable to or surpassing those of models trained on matched realistic datasets, reducing the reliance on real-world MRI data by up to 96%.
Dynamic Multimodal Information Bottleneck for Multimodality Classification
Specifically, our information bottleneck module serves to filter out the task-irrelevant information and noises in the fused feature, and we further introduce a sufficiency loss to prevent dropping of task-relevant information, thus explicitly preserving the sufficiency of prediction information in the distilled feature.
Stain Consistency Learning: Handling Stain Variation for Automatic Digital Pathology Segmentation
Numerous methods have been developed to improve the robustness of machine learning methods to stain variation, but comparative studies have demonstrated limited benefits to performance.
Calibrating the Dice loss to handle neural network overconfidence for biomedical image segmentation
However, it is well known that the DSC loss is poorly calibrated, resulting in overconfident predictions that cannot be usefully interpreted in biomedical and clinical practice.
Explainable COVID-19 Infections Identification and Delineation Using Calibrated Pseudo Labels
We demonstrate the effectiveness of our model with the combination of limited labelled data and sufficient unlabelled data or weakly-labelled data.
CS$^2$: A Controllable and Simultaneous Synthesizer of Images and Annotations with Minimal Human Intervention
The destitution of image data and corresponding expert annotations limit the training capacities of AI diagnostic models and potentially inhibit their performance.
The Beauty or the Beast: Which Aspect of Synthetic Medical Images Deserves Our Focus?
Training medical AI algorithms requires large volumes of accurately labeled datasets, which are difficult to obtain in the real world.
Multi-scale, Data-driven and Anatomically Constrained Deep Learning Image Registration for Adult and Fetal Echocardiography
We propose a framework that combines three strategies for DLIR in both fetal and adult echo: (1) an anatomic shape-encoded loss to preserve physiological myocardial and left ventricular anatomical topologies in warped images; (2) a data-driven loss that is trained adversarially to preserve good image texture features in warped images; and (3) a multi-scale training scheme of a data-driven and anatomically constrained algorithm to improve accuracy.
Multiview Two-Task Recursive Attention Model for Left Atrium and Atrial Scars Segmentation
Late Gadolinium Enhanced Cardiac MRI (LGE-CMRI) for detecting atrial scars in atrial fibrillation (AF) patients has recently emerged as a promising technique to stratify patients, guide ablation therapy and predict treatment success.
Stochastic Deep Compressive Sensing for the Reconstruction of Diffusion Tensor Cardiac MRI
To the best of our knowledge, this is the first study using deep CNN based CS for the DT-CMR reconstruction.
A two-stage 3D Unet framework for multi-class segmentation on full resolution image
Deep convolutional neural networks (CNNs) have been intensively used for multi-class segmentation of data from different modalities and achieved state-of-the-art performances.
Revisiting Skip-Gram Negative Sampling Model with Rectification
We revisit skip-gram negative sampling (SGNS), one of the most popular neural-network based approaches to learning distributed word representation.
Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks
In this context, a reliable fully automatic segmentation method for the brain tumor segmentation is necessary for an efficient measurement of the tumor extent.
Fully Automatic Segmentation and Objective Assessment of Atrial Scars for Longstanding Persistent Atrial Fibrillation Patients Using Late Gadolinium-Enhanced MRI
In this study, we proposed a novel fully automatic pipeline to achieve an accurate and objective atrial scarring segmentation and assessment of LGE MRI scans for the AF patients.
Deep De-Aliasing for Fast Compressive Sensing MRI
Fast Magnetic Resonance Imaging (MRI) is highly in demand for many clinical applications in order to reduce the scanning cost and improve the patient experience.
The Deep Poincaré Map: A Novel Approach for Left Ventricle Segmentation
Our method is evaluated on two datasets, namely the Sunnybrook Cardiac Dataset (SCD) and data from the STACOM 2011 LV segmentation challenge.
Neural Networks Designing Neural Networks: Multi-Objective Hyper-Parameter Optimization
The method is evaluated on the MNIST and CIFAR-10 image datasets, optimizing for both recognition accuracy and computational complexity.
Numerical Methods for Coupled Reconstruction and Registration in Digital Breast Tomosynthesis
We evaluate our methods using various computational digital phantoms, uncompressed breast MR images, and in-vivo DBT simulations.
Towards Practical Visual Search Engine within Elasticsearch
In this paper, we describe our end-to-end content-based image retrieval system built upon Elasticsearch, a well-known and popular textual search engine.
Generating Magnetic Resonance Spectroscopy Imaging Data of Brain Tumours from Linear, Non-Linear and Deep Learning Models
Magnetic Resonance Spectroscopy (MRS) provides valuable information to help with the identification and understanding of brain tumors, yet MRS is not a widely available medical imaging modality.
A Machine Learning Approach to Shipping Box Design
Having the right assortment of shipping boxes in the fulfillment warehouse to pack and ship customer's online orders is an indispensable and integral part of nowadays eCommerce business, as it will not only help maintain a profitable business but also create great experiences for customers.
Atrial scars segmentation via potential learning in the graph-cuts framework
Late Gadolinium Enhancement Magnetic Resonance Imaging (LGE MRI) emerged as a routine scan for patients with atrial fibrillation (AF).
Atrial fibrosis quantification based on maximum likelihood estimator of multivariate images
We present a fully-automated segmentation and quantification of the left atrial (LA) fibrosis and scars combining two cardiac MRIs, one is the target late gadolinium-enhanced (LGE) image, and the other is an anatomical MRI from the same acquisition session.
Atrial Scar Quantification via Multi-scale CNN in the Graph-cuts Framework
Compared with the conventional methods, which are based on the manual delineation of LA for initialization, our method is fully automatic and has demonstrated significantly better Dice score and accuracy (p < 0. 01).
Evaluation of Algorithms for Multi-Modality Whole Heart Segmentation: An Open-Access Grand Challenge
This manuscript presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017.
Fast and Exact Nearest Neighbor Search in Hamming Space on Full-Text Search Engines
A growing interest has been witnessed recently from both academia and industry in building nearest neighbor search (NNS) solutions on top of full-text search engines.
Direct Quantification for Coronary Artery Stenosis Using Multiview Learning
The proposed DMQCA model consists of a multiview module with two attention mechanisms, a key-frame module, and a regression module, to achieve direct accurate multiple-index estimation.
Discriminative Consistent Domain Generation for Semi-supervised Learning
Based on the generated discriminative consistent domain, we can use the unlabeled data to learn the task model along with the labeled data via a consistent image generation.
Recurrent Aggregation Learning for Multi-View Echocardiographic Sequences Segmentation
Our method is built as an end-to-end framework for segmentation and classification.
MRI Brain Tumor Segmentation using Random Forests and Fully Convolutional Networks
In this paper, we propose a novel learning based method for automated segmentation of brain tumor in multimodal MRI images, which incorporates two sets of machine -learned and hand crafted features.
Distributed and Consistent Multi-Image Feature Matching via QuickMatch
The centralized QuickMatch algorithm is compared to other standard matching algorithms, while the Distributed QuickMatch algorithm is compared to the centralized algorithm in terms of preservation of match consistency.
Automatic Prostate Zonal Segmentation Using Fully Convolutional Network with Feature Pyramid Attention
Expert 1) were 0. 71 (PZ) and 0. 75 (TZ).
Real-Time Edge Intelligence in the Making: A Collaborative Learning Framework via Federated Meta-Learning
Further, we investigate the convergence of the proposed federated meta-learning algorithm under mild conditions on node similarity and the adaptation performance at the target edge.
Simultaneous Left Atrium Anatomy and Scar Segmentations via Deep Learning in Multiview Information with Attention
Using our MVTT recursive attention model, both the LA anatomy and scar can be segmented accurately (mean Dice score of 93% for the LA anatomy and 87% for the scar segmentations) and efficiently (~0. 27 seconds to simultaneously segment the LA anatomy and scars directly from the 3D LGE CMR dataset with 60-68 2D slices).
Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification from CT Images
An outbreak of a novel coronavirus disease (i. e., COVID-19) has been recorded in Wuhan, China since late December 2019, which subsequently became pandemic around the world.
Gleason Score Prediction using Deep Learning in Tissue Microarray Image
Prostate cancer (PCa) is one of the most common cancers in men around the world.
Deep Attentive Wasserstein Generative Adversarial Networks for MRI Reconstruction with Recurrent Context-Awareness
The performance of traditional compressive sensing-based MRI (CS-MRI) reconstruction is affected by its slow iterative procedure and noise-induced artefacts.
Annealing Genetic GAN for Minority Oversampling
In particular, the generator uses different training strategies to generate multiple offspring and retain the best.
Automated Multi-Channel Segmentation for the 4D Myocardial Velocity Mapping Cardiac MR
Based on the results, our method provides compelling evidence for the design and application for the multi-channel image analysis of the 4D MVM CMR data.
Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyond
Explainable Artificial Intelligence (XAI) is an emerging research topic of machine learning aimed at unboxing how AI systems' black-box choices are made.
MV-RAN: Multiview recurrent aggregation network for echocardiographic sequences segmentation and full cardiac cycle analysis
In addition, for a better interpretation of pathophysiological processes, clinical decision-making and prognosis, such cardiac anatomy segmentation and quantitative analysis of various clinical indices should ideally be performed for the data covering the full cardiac cycle.
Three-Dimensional Embedded Attentive RNN (3D-EAR) Segmentor for Left Ventricle Delineation from Myocardial Velocity Mapping
Myocardial Velocity Mapping Cardiac MR (MVM-CMR) can be used to measure global and regional myocardial velocities with proved reproducibility.
Explainable AI For COVID-19 CT Classifiers: An Initial Comparison Study
XAI is an AI model that is programmed to explain its goals, logic, and decision making so that the end users can understand.
Decision Making
Explainable Artificial Intelligence (XAI)
+1
JAS-GAN: Generative Adversarial Network Based Joint Atrium and Scar Segmentations on Unbalanced Atrial Targets
In this paper, we propose an inter-cascade generative adversarial network, namely JAS-GAN, to segment the unbalanced atrial targets from LGE CMR images automatically and accurately in an end-to-end way.
Generative Adversarial Networks (GAN) Powered Fast Magnetic Resonance Imaging -- Mini Review, Comparison and Perspectives
However, one drawback of MRI is its comparatively slow scanning and reconstruction compared to other image modalities, limiting its usage in some clinical applications when imaging time is critical.
Recent Advances in Fibrosis and Scar Segmentation from Cardiac MRI: A State-of-the-Art Review and Future Perspectives
Late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) has been successful for its efficacy in guiding the clinical diagnosis and treatment reliably.
RingFed: Reducing Communication Costs in Federated Learning on Non-IID Data
Federated learning is a widely used distributed deep learning framework that protects the privacy of each client by exchanging model parameters rather than raw data.
High-Resolution Pelvic MRI Reconstruction Using a Generative Adversarial Network with Attention and Cyclic Loss
Magnetic resonance imaging (MRI) is an important medical imaging modality, but its acquisition speed is quite slow due to the physiological limitations.
3D AGSE-VNet: An Automatic Brain Tumor MRI Data Segmentation Framework
Background: Glioma is the most common brain malignant tumor, with a high morbidity rate and a mortality rate of more than three percent, which seriously endangers human health.
Ranked #1 on
Brain Tumor Segmentation
on 768 chest X-ray images
FA-GAN: Fused Attentive Generative Adversarial Networks for MRI Image Super-Resolution
High-resolution magnetic resonance images can provide fine-grained anatomical information, but acquiring such data requires a long scanning time.
Synthetic Velocity Mapping Cardiac MRI Coupled with Automated Left Ventricle Segmentation
Temporal patterns of cardiac motion provide important information for cardiac disease diagnosis.
Temporal Cue Guided Video Highlight Detection With Low-Rank Audio-Visual Fusion
Video highlight detection plays an increasingly important role in social media content filtering, however, it remains highly challenging to develop automated video highlight detection methods because of the lack of temporal annotations (i. e., where the highlight moments are in long videos) for supervised learning.
Focal Attention Networks: optimising attention for biomedical image segmentation
In recent years, there has been increasing interest to incorporate attention into deep learning architectures for biomedical image segmentation.
Incorporating Boundary Uncertainty into loss functions for biomedical image segmentation
Manual segmentation is used as the gold-standard for evaluating neural networks on automated image segmentation tasks.
Robust Weakly Supervised Learning for COVID-19 Recognition Using Multi-Center CT Images
The multi-domain shift problem for the multi-center and multi-scanner studies is therefore nontrivial that is also crucial for a dependable recognition and critical for reproducible and objective diagnosis and prognosis.
Edge-Enhanced Dual Discriminator Generative Adversarial Network for Fast MRI with Parallel Imaging Using Multi-view Information
The dual discriminator design aims to improve the edge information in MRI reconstruction.
AI-based Reconstruction for Fast MRI -- A Systematic Review and Meta-analysis
Compressed sensing (CS) has been playing a key role in accelerating the magnetic resonance imaging (MRI) acquisition process.
Data Harmonisation for Information Fusion in Digital Healthcare: A State-of-the-Art Systematic Review, Meta-Analysis and Future Research Directions
Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness.
Online Attentive Kernel-Based Temporal Difference Learning
Moreover, in learning sparse representations, attention mechanisms are utilized to represent the degree of sparsification, and a smooth attentive function is introduced into the kernel-based VFA.
AI-based Medical e-Diagnosis for Fast and Automatic Ventricular Volume Measurement in the Patients with Normal Pressure Hydrocephalus
Finally, we verify the reliability of the model and achieved automatic measurement of VV and ICV.
DocBed: A Multi-Stage OCR Solution for Documents with Complex Layouts
Digitization of newspapers is of interest for many reasons including preservation of history, accessibility and search ability, etc.
Unsupervised Image Registration Towards Enhancing Performance and Explainability in Cardiac And Brain Image Analysis
Moreover, inverse-consistency is a fundamental inter-modality registration property that is not considered in deep learning registration algorithms.
Automatic Fine-grained Glomerular Lesion Recognition in Kidney Pathology
Recognition of glomeruli lesions is the key for diagnosis and treatment planning in kidney pathology; however, the coexisting glomerular structures such as mesangial regions exacerbate the difficulties of this task.
ME-Net: Multi-Encoder Net Framework for Brain Tumor Segmentation
Moreover, the serious voxel imbalance between the brain tumor and the background as well as the different sizes and locations of the brain tumor makes the segmentation of 3D images a challenging problem.
Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and Methodologies from CNN, GAN to Attention and Transformers
Research studies have shown no qualms about using data driven deep learning models for downstream tasks in medical image analysis, e. g., anatomy segmentation and lesion detection, disease diagnosis and prognosis, and treatment planning.
Towards Reliable and Explainable AI Model for Solid Pulmonary Nodule Diagnosis
However, detection and accurate diagnosis of pulmonary nodules depend heavily on the experiences of radiologists and can be a heavy workload for them.
Faster Diffusion Cardiac MRI with Deep Learning-based breath hold reduction
Diffusion Tensor Cardiac Magnetic Resonance (DT-CMR) enables us to probe the microstructural arrangement of cardiomyocytes within the myocardium in vivo and non-invasively, which no other imaging modality allows.
Explainable AI (XAI) in Biomedical Signal and Image Processing: Promises and Challenges
This paper aims at providing an overview on XAI in biomedical data processing and points to an upcoming Special Issue on Deep Learning in Biomedical Image and Signal Processing of the IEEE Signal Processing Magazine that is going to appear in March 2022.
Large-Kernel Attention for 3D Medical Image Segmentation
The performance improvement due to the proposed LK attention module was also statistically validated.
Human Treelike Tubular Structure Segmentation: A Comprehensive Review and Future Perspectives
Various structures in human physiology follow a treelike morphology, which often expresses complexity at very fine scales.
A Novel Automated Classification and Segmentation for COVID-19 using 3D CT Scans
Medical image classification and segmentation based on deep learning (DL) are emergency research topics for diagnosing variant viruses of the current COVID-19 situation.
Unsupervised Tissue Segmentation via Deep Constrained Gaussian Network
However, most machine and deep learning based approaches are supervised and developed using a large number of training samples, in which the pixelwise annotations are expensive and sometimes can be impossible to obtain.
Region-Based Evidential Deep Learning to Quantify Uncertainty and Improve Robustness of Brain Tumor Segmentation
Despite recent advances in the accuracy of brain tumor segmentation, the results still suffer from low reliability and robustness.
A survey, review, and future trends of skin lesion segmentation and classification
The Computer-aided Diagnosis or Detection (CAD) approach for skin lesion analysis is an emerging field of research that has the potential to alleviate the burden and cost of skin cancer screening.
Delving into the Frequency: Temporally Consistent Human Motion Transfer in the Fourier Space
To close the frequency gap between the natural and synthetic videos, we propose a novel Frequency-based human MOtion TRansfer framework, named FreMOTR, which can effectively mitigate the spatial artifacts and the temporal inconsistency of the synthesized videos.
Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation
especially for cohorts with different lung diseases.
Non-Imaging Medical Data Synthesis for Trustworthy AI: A Comprehensive Survey
Data quality is the key factor for the development of trustworthy AI in healthcare.
Review of data types and model dimensionality for cardiac DTI SMS-related artefact removal
As diffusion tensor imaging (DTI) gains popularity in cardiac imaging due to its unique ability to non-invasively assess the cardiac microstructure, deep learning-based Artificial Intelligence is becoming a crucial tool in mitigating some of its drawbacks, such as the long scan times.
MCS: An In-battle Commentary System for MOBA Games
This paper introduces a generative system for in-battle real-time commentary in mobile MOBA games.
Adversarial Transformer for Repairing Human Airway Segmentation
Discontinuity in the delineation of peripheral bronchioles hinders the potential clinical application of automated airway segmentation models.
Deep Kronecker Network
As such, we propose DKN, that is able to i) adapt to low sample size limitation, ii) provide desired model interpretation, and iii) achieve the prediction power as CNN.
Video Event Extraction via Tracking Visual States of Arguments
Video event extraction aims to detect salient events from a video and identify the arguments for each event as well as their semantic roles.
AMD: Adaptive Masked Distillation for Object Detection
As a general model compression paradigm, feature-based knowledge distillation allows the student model to learn expressive features from the teacher counterpart.
Hierarchical Perception Adversarial Learning Framework for Compressed Sensing MRI
HP-ALF can perceive the image information in the hierarchical mechanism: image-level perception and patch-level perception.
ViGU: Vision GNN U-Net for Fast MRI
The majority of existing deep learning models, e. g., convolutional neural networks, work on data with Euclidean or regular grids structures.
Swin Deformable Attention Hybrid U-Net for Medical Image Segmentation
Medical image segmentation is a crucial task in the field of medical image analysis.
Online Control Barrier Functions for Decentralized Multi-Agent Navigation
Control barrier functions (CBFs) enable guaranteed safe multi-agent navigation in the continuous domain.
Less is More: Unsupervised Mask-guided Annotated CT Image Synthesis with Minimum Manual Segmentations
To address these issues, in this work, we propose a novel strategy for medical image synthesis, namely Unsupervised Mask (UM)-guided synthesis, to obtain both synthetic images and segmentations using limited manual segmentation labels.
Deep Learning-based Diffusion Tensor Cardiac Magnetic Resonance Reconstruction: A Comparison Study
Our results indicate that the models we discussed in this study can be applied for clinical use at an acceleration factor (AF) of $\times 2$ and $\times 4$, with the D5C5 model showing superior fidelity for reconstruction and the SwinMR model providing higher perceptual scores.
You Don't Have to Be Perfect to Be Amazing: Unveil the Utility of Synthetic Images
In addition, we have empirically demonstrated that the utility score does not require images with both high fidelity and high variety.
$\mathrm{SAM^{Med}}$: A medical image annotation framework based on large vision model
The $\mathrm{SAM^{assist}}$ demonstrates the generalization ability of SAM to the downstream medical segmentation task using the prompt-learning approach.
Enhancing Super-Resolution Networks through Realistic Thick-Slice CT Simulation
The generated images were then leveraged to train four distinct super-resolution (SR) models, which were subsequently evaluated using the real thick-slice images from the 2016 Low Dose CT Grand Challenge dataset.
Is attention all you need in medical image analysis? A review
Attention and Transformer compartments (Transf/Attention) which can well maintain properties for modelling global relationships, have been proposed as lighter alternatives of full Transformers.
Video-Instrument Synergistic Network for Referring Video Instrument Segmentation in Robotic Surgery
Our method is verified on these datasets, and experimental results exhibit that the VIS-Net can significantly outperform existing state-of-the-art referring segmentation methods.
DMKD: Improving Feature-based Knowledge Distillation for Object Detection Via Dual Masking Augmentation
Recent mainstream masked distillation methods function by reconstructing selectively masked areas of a student network from the feature map of its teacher counterpart.
Real-Time Non-Invasive Imaging and Detection of Spreading Depolarizations through EEG: An Ultra-Light Explainable Deep Learning Approach
This study presented a novel ultra-light-weight multi-modal deep-learning network to fuse EEG spectrogram imaging and temporal power vectors to enhance SD identification accuracy over each single electrode, allowing flexible EEG map and paving the way for SD detection on ultra-low-density EEG with variable electrode positioning.
Efficient Post-processing of Diffusion Tensor Cardiac Magnetic Imaging Using Texture-conserving Deformable Registration
Diffusion tensor cardiac magnetic resonance (DT-CMR) is a method capable of providing non-invasive measurements of myocardial microstructure.
Style Transfer and Self-Supervised Learning Powered Myocardium Infarction Super-Resolution Segmentation
This study proposes a pipeline that incorporates a novel style transfer model and a simultaneous super-resolution and segmentation model.
T1/T2 relaxation temporal modelling from accelerated acquisitions using a Latent Transformer
Quantitative cardiac magnetic resonance T1 and T2 mapping enable myocardial tissue characterisation but the lengthy scan times restrict their widespread clinical application.
High Accuracy and Cost-Saving Active Learning 3D WD-UNet for Airway Segmentation
We propose a novel Deep Active Learning (DeepAL) model-3D Wasserstein Discriminative UNet (WD-UNet) for reducing the annotation effort of medical 3D Computed Tomography (CT) segmentation.
Exploiting User Comments for Early Detection of Fake News Prior to Users' Commenting
To break such a dilemma, a feasible but not well-studied solution is to leverage social contexts (e. g., comments) from historical news for training a detection model and apply it to newly emerging news without social contexts.
Data-Free Distillation Improves Efficiency and Privacy in Federated Thorax Disease Analysis
Thorax disease analysis in large-scale, multi-centre, and multi-scanner settings is often limited by strict privacy policies.
High-Resolution Reference Image Assisted Volumetric Super-Resolution of Cardiac Diffusion Weighted Imaging
This study proposed a novel framework to enable volumetric super-resolution, with an additional model input of high-resolution b0 DWI.
The Missing U for Efficient Diffusion Models
Diffusion Probabilistic Models stand as a critical tool in generative modelling, enabling the generation of complex data distributions.
Post-COVID Highlights: Challenges and Solutions of AI Techniques for Swift Identification of COVID-19
Since the onset of the COVID-19 pandemic in 2019, there has been a concerted effort to develop cost-effective, non-invasive, and rapid AI-based tools.
Machine-Learned Atomic Cluster Expansion Potentials for Fast and Quantum-Accurate Thermal Simulations of Wurtzite AlN
Using the atomic cluster expansion (ACE) framework, we develop a machine learning interatomic potential for fast and accurately modelling the phonon transport properties of wurtzite aluminum nitride.
Where to Begin? From Random to Foundation Model Instructed Initialization in Federated Learning for Medical Image Segmentation
In medical image analysis, Federated Learning (FL) stands out as a key technology that enables privacy-preserved, decentralized data processing, crucial for handling sensitive medical data.
Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge
The online validation set incorporated 52 HRCT scans from patients with fibrotic lung disease and the offline test set included 140 cases from fibrosis and COVID-19 patients.
Data and Physics driven Deep Learning Models for Fast MRI Reconstruction: Fundamentals and Methodologies
Magnetic Resonance Imaging (MRI) is a pivotal clinical diagnostic tool, yet its extended scanning times often compromise patient comfort and image quality, especially in volumetric, temporal and quantitative scans.
Assessing the Efficacy of Invisible Watermarks in AI-Generated Medical Images
AI-generated medical images are gaining growing popularity due to their potential to address the data scarcity challenge in the real world.
HAMLET: Graph Transformer Neural Operator for Partial Differential Equations
We present a novel graph transformer framework, HAMLET, designed to address the challenges in solving partial differential equations (PDEs) using neural networks.
Make it more specific: A novel uncertainty based airway segmentation application on 3D U-Net and its variants
The most popular algorithms in medical segmentation, 3D U-Net and its variants, can directly implement the task of lung trachea segmentation, but its failure to consider the special tree-like structure of the trachea suggests that there is much room for improvement in its segmentation accuracy.
Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRI
This challenge leads to necessitate extensive training data in many deep learning reconstruction methods.
MambaMIR: An Arbitrary-Masked Mamba for Joint Medical Image Reconstruction and Uncertainty Estimation
The recent Mamba model has shown remarkable adaptability for visual representation learning, including in medical imaging tasks.
ContrastDiagnosis: Enhancing Interpretability in Lung Nodule Diagnosis Using Contrastive Learning
This framework is designed to introduce inherent transparency and provide extensive post-hoc explainability for deep learning model, making them more suitable for clinical medical diagnosis.
Co-Optimization of Environment and Policies for Decentralized Multi-Agent Navigation
By introducing two sub-objectives of multi-agent navigation and environment optimization, we propose an $\textit{agent-environment co-optimization}$ problem and develop a $\textit{coordinated algorithm}$ that alternates between these sub-objectives to search for an optimal synthesis of agent actions and obstacle configurations in the environment; ultimately, improving the navigation performance.
The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023
To address this issue, we organized the Cardiac MRI Reconstruction Challenge (CMRxRecon) in 2023, in collaboration with the 26th International Conference on MICCAI.
