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Found 193 papers, 55 papers with code
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.
Contrast-Free Myocardial Scar Segmentation in Cine MRI using Motion and Texture Fusion
Late gadolinium enhancement MRI (LGE MRI) is the gold standard for the detection of myocardial scars for post myocardial infarction (MI).
Less is More: Towards Green Code Large Language Models via Unified Structural Pruning
The extensive application of Large Language Models (LLMs) in generative coding tasks has raised concerns due to their high computational demands and energy consumption.
RoboMIND: Benchmark on Multi-embodiment Intelligence Normative Data for Robot Manipulation
Developing robust and general-purpose robotic manipulation policies is a key goal in the field of robotics.
Are the Latent Representations of Foundation Models for Pathology Invariant to Rotation?
Self-supervised foundation models for digital pathology encode small patches from H\&E whole slide images into latent representations used for downstream tasks.
LAION-SG: An Enhanced Large-Scale Dataset for Training Complex Image-Text Models with Structural Annotations
However, existing T2I models show decayed performance in compositional image generation involving multiple objects and intricate relationships.
Anatomy-Guided Radiology Report Generation with Pathology-Aware Regional Prompts
Radiology reporting generative AI holds significant potential to alleviate clinical workloads and streamline medical care.
Decoding Report Generators: A Cyclic Vision-Language Adapter for Counterfactual Explanations
Despite significant advancements in report generation methods, a critical limitation remains: the lack of interpretability in the generated text.
Enhancing Weakly Supervised Semantic Segmentation for Fibrosis via Controllable Image Generation
Fibrotic Lung Disease (FLD) is a severe condition marked by lung stiffening and scarring, leading to respiratory decline.
Deep Generative Models Unveil Patterns in Medical Images Through Vision-Language Conditioning
Deep generative models have significantly advanced medical imaging analysis by enhancing dataset size and quality.
Arbitrarily-Conditioned Multi-Functional Diffusion for Multi-Physics Emulation
ACMFD can perform a wide range of tasks within a single framework, including forward prediction, various inverse problems, and simulating data for entire systems or subsets of quantities conditioned on others.
Toward Efficient Kernel-Based Solvers for Nonlinear PDEs
In numerical experiments, we demonstrate the advantages of our method in solving several benchmark PDEs.
Preserving Cardiac Integrity: A Topology-Infused Approach to Whole Heart Segmentation
Whole heart segmentation (WHS) supports cardiovascular disease (CVD) diagnosis, disease monitoring, treatment planning, and prognosis.
Mamba Neural Operator: Who Wins? Transformers vs. State-Space Models for PDEs
Partial differential equations (PDEs) are widely used to model complex physical systems, but solving them efficiently remains a significant challenge.
From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare
Federated learning holds great potential for enabling large-scale healthcare research and collaboration across multiple centres while ensuring data privacy and security are not compromised.
Infrared and Visible Image Fusion with Hierarchical Human Perception
Existing methods take pixel intensity, texture and high-level vision task information as the standards to determine preservation of information, lacking enhancement for human perception.
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.
Coupling AI and Citizen Science in Creation of Enhanced Training Dataset for Medical Image Segmentation
Recent advancements in medical imaging and artificial intelligence (AI) have greatly enhanced diagnostic capabilities, but the development of effective deep learning (DL) models is still constrained by the lack of high-quality annotated datasets.
CT-SDM: A Sampling Diffusion Model for Sparse-View CT Reconstruction across All Sampling Rates
Because of the reduced number of projection views, traditional reconstruction methods can lead to severe artifacts.
Learning Task-Specific Sampling Strategy for Sparse-View CT Reconstruction
Thus, a task-specific sampling strategy can be applied for each type of scans to improve the quality of SVCT imaging and further assist in performance of downstream clinical usage.
Explicit Differentiable Slicing and Global Deformation for Cardiac Mesh Reconstruction
Here, we propose a novel explicit differentiable voxelization and slicing (DVS) algorithm that allows gradient backpropagation to a mesh from its slices, facilitating refined mesh optimization directly supervised by the losses defined on 2D images.
McCaD: Multi-Contrast MRI Conditioned, Adaptive Adversarial Diffusion Model for High-Fidelity MRI Synthesis
Moreover, existing methods for multi-contrast MRI synthesis often fail to accurately map feature-level information across multiple imaging contrasts.
Toward a More Complete OMR Solution
One approach to tackle OMR is through a multi-stage pipeline, where the system first detects visual music notation elements in the image (object detection) and then assembles them into a music notation (notation assembly).
Distribution Backtracking Builds A Faster Convergence Trajectory for Diffusion Distillation
However, there is a score mismatch issue in the early stage of the distillation process, because existing methods mainly focus on using the endpoint of pre-trained diffusion models as teacher models, overlooking the importance of the convergence trajectory between the student generator and the teacher model.
Hokoff: Real Game Dataset from Honor of Kings and its Offline Reinforcement Learning Benchmarks
The advancement of Offline Reinforcement Learning (RL) and Offline Multi-Agent Reinforcement Learning (MARL) critically depends on the availability of high-quality, pre-collected offline datasets that represent real-world complexities and practical applications.
Beyond the Hype: A dispassionate look at vision-language models in medical scenario
RadVUQA mainly validates LVLMs across five dimensions: 1) Anatomical understanding, assessing the models' ability to visually identify biological structures; 2) Multimodal comprehension, which involves the capability of interpreting linguistic and visual instructions to produce desired outcomes; 3) Quantitative and spatial reasoning, evaluating the models' spatial awareness and proficiency in combining quantitative analysis with visual and linguistic information; 4) Physiological knowledge, measuring the models' capability to comprehend functions and mechanisms of organs and systems; and 5) Robustness, which assesses the models' capabilities against unharmonised and synthetic data.
SeLoRA: Self-Expanding Low-Rank Adaptation of Latent Diffusion Model for Medical Image Synthesis
The persistent challenge of medical image synthesis posed by the scarcity of annotated data and the need to synthesize `missing modalities' for multi-modal analysis, underscored the imperative development of effective synthesis methods.
Advancing oncology with federated learning: transcending boundaries in breast, lung, and prostate cancer. A systematic review
Federated Learning (FL) has emerged as a promising solution to address the limitations of centralised machine learning (ML) in oncology, particularly in overcoming privacy concerns and harnessing the power of diverse, multi-center data.
A dual-task mutual learning framework for predicting post-thrombectomy cerebral hemorrhage
Ischemic stroke is a severe condition caused by the blockage of brain blood vessels, and can lead to the death of brain tissue due to oxygen deprivation.
CIResDiff: A Clinically-Informed Residual Diffusion Model for Predicting Idiopathic Pulmonary Fibrosis Progression
Specifically, from the clinical prior knowledge, we tailor improvements to the traditional diffusion model and propose a Clinically-Informed Residual Diffusion model, called CIResDiff.
Artificial Immunofluorescence in a Flash: Rapid Synthetic Imaging from Brightfield Through Residual Diffusion
This paper presents a novel method for the conditional synthesis of IF images directly from BF images along with cell segmentation masks.
Differentiable Voxelization and Mesh Morphing
In this paper, we propose the differentiable voxelization of 3D meshes via the winding number and solid angles.
LGRNet: Local-Global Reciprocal Network for Uterine Fibroid Segmentation in Ultrasound Videos
To this end, we collect and annotate the first ultrasound video dataset with 100 videos for uterine fibroid segmentation (UFUV).
Ranked #1 on
Video Polyp Segmentation
on SUN-SEG-Easy
Probing Perfection: The Relentless Art of Meddling for Pulmonary Airway Segmentation from HRCT via a Human-AI Collaboration Based Active Learning Method
In pulmonary tracheal segmentation, the scarcity of annotated data is a prevalent issue in medical segmentation.
CMRxRecon2024: A Multi-Modality, Multi-View K-Space Dataset Boosting Universal Machine Learning for Accelerated Cardiac MRI
To the best of our knowledge, the CMRxRecon2024 dataset is the largest and most protocal-diverse publicly available cardiac k-space dataset.
Crafting Customisable Characters with LLMs: Introducing SimsChat, a Persona-Driven Role-Playing Agent Framework
Large Language Models (LLMs) demonstrate remarkable ability to comprehend instructions and generate human-like text, enabling sophisticated agent simulation beyond basic behavior replication.
Diff3Dformer: Leveraging Slice Sequence Diffusion for Enhanced 3D CT Classification with Transformer Networks
The manifestation of symptoms associated with lung diseases can vary in different depths for individual patients, highlighting the significance of 3D information in CT scans for medical image classification.
Fuzzy Attention-based Border Rendering Network for Lung Organ Segmentation
Automatic lung organ segmentation on CT images is crucial for lung disease diagnosis.
DiffExplainer: Unveiling Black Box Models Via Counterfactual Generation
To address this limitation, we propose an agent model capable of generating counterfactual images that prompt different decisions when plugged into a black box model.
Low-rank based motion correction followed by automatic frame selection in DT-CMR
Motivation: Post-processing of in-vivo diffusion tensor CMR (DT-CMR) is challenging due to the low SNR and variation in contrast between frames which makes image registration difficult, and the need to manually reject frames corrupted by motion.
Groupwise Deformable Registration of Diffusion Tensor Cardiovascular Magnetic Resonance: Disentangling Diffusion Contrast, Respiratory and Cardiac Motions
Diffusion tensor based cardiovascular magnetic resonance (DT-CMR) offers a non-invasive method to visualize the myocardial microstructure.
Can Generative AI Replace Immunofluorescent Staining Processes? A Comparison Study of Synthetically Generated CellPainting Images from Brightfield
Cell imaging assays utilizing fluorescence stains are essential for observing sub-cellular organelles and their responses to perturbations.
Deep asymmetric mixture model for unsupervised cell segmentation
To address these issues, this paper presents a novel asymmetric mixture model for unsupervised cell segmentation.
Enhancing Global Sensitivity and Uncertainty Quantification in Medical Image Reconstruction with Monte Carlo Arbitrary-Masked Mamba
In this study, we introduce MambaMIR, an Arbitrary-Masked Mamba-based model with wavelet decomposition for joint medical image reconstruction and uncertainty estimation.
Decoding Decision Reasoning: A Counterfactual-Powered Model for Knowledge Discovery
In medical imaging, particularly in early disease detection and prognosis tasks, discerning the rationale behind an AI model's predictions is crucial for evaluating the reliability of its decisions.
When AI Eats Itself: On the Caveats of AI Autophagy
Generative Artificial Intelligence (AI) technologies and large models are producing realistic outputs across various domains, such as images, text, speech, and music.
4DBInfer: A 4D Benchmarking Toolbox for Graph-Centric Predictive Modeling on Relational DBs
Although RDBs store vast amounts of rich, informative data spread across interconnected tables, the progress of predictive machine learning models as applied to such tasks arguably falls well behind advances in other domains such as computer vision or natural language processing.
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.
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.
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.
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.
Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRI
This challenge necessitates extensive training data in deep learning reconstruction methods.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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%.
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.
$\mathrm{SAM^{Med}}$: 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
Thus, we introduce a simple yet realistic method to generate thick CT images from thin-slice CT images, facilitating the creation of training pairs for SR algorithms.
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 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.
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.
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.
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.
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.
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.
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.
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.
Online Control Barrier Functions for Decentralized Multi-Agent Navigation
Control barrier functions (CBFs) enable guaranteed safe multi-agent navigation in the continuous domain.
Swin Deformable Attention Hybrid U-Net for Medical Image Segmentation
Medical image segmentation is a crucial task in the field of medical image analysis.
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.
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.
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.
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.
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.
Adversarial Transformer for Repairing Human Airway Segmentation
Discontinuity in the delineation of peripheral bronchioles hinders the potential clinical application of automated airway segmentation models.
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.
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.
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.
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 Quad-Bayer Re-mosaic: Dataset and Report
A detailed description of all models developed in this challenge is provided in this paper.
Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation
especially for cohorts with different lung diseases.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.