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Found 81 papers, 27 papers with code
PathSeqSAM: Sequential Modeling for Pathology Image Segmentation with SAM2
Current methods for pathology image segmentation typically treat 2D slices independently, ignoring valuable cross-slice information.
CTI-Unet: Cascaded Threshold Integration for Improved U-Net Segmentation of Pathology Images
Chronic kidney disease (CKD) is a growing global health concern, necessitating precise and efficient image analysis to aid diagnosis and treatment planning.
Target-aware Bidirectional Fusion Transformer for Aerial Object Tracking
The trackers based on lightweight neural networks have achieved great success in the field of aerial remote sensing, most of which aggregate multi-stage deep features to lift the tracking quality.
Aerial Secure Collaborative Communications under Eavesdropper Collusion in Low-altitude Economy: A Generative Swarm Intelligent Approach
Specifically, we consider a two-way DCB-enabled aerial communication between two AAV swarms and construct these swarms as two AAV virtual antenna arrays.
Generative AI-enabled Wireless Communications for Robust Low-Altitude Economy Networking
In this paper, we investigate a novel application of Generative Artificial Intelligence (GenAI) to improve the robustness of LAENets.
Generative AI Enabled Robust Data Augmentation for Wireless Sensing in ISAC Networks
Building on this, the scheme further utilizes another diffusion model to enhance the sample quality, thereby facilitating the data augmentation in scenarios where the original sensing data is insufficient and unevenly distributed.
Intelligent Mobile AI-Generated Content Services via Interactive Prompt Engineering and Dynamic Service Provisioning
Due to massive computational demands of large generative models, AI-Generated Content (AIGC) can organize collaborative Mobile AIGC Service Providers (MASPs) at network edges to provide ubiquitous and customized content generation for resource-constrained users.
AoI-Sensitive Data Forwarding with Distributed Beamforming in UAV-Assisted IoT
This paper proposes a UAV-assisted forwarding system based on distributed beamforming to enhance age of information (AoI) in Internet of Things (IoT).
CASC-AI: Consensus-aware Self-corrective AI Agents for Noise Cell Segmentation
The Consensus Matrix defines regions where both the AI and annotators agree on cell and non-cell annotations, which are prioritized with stronger supervision.
UAV-assisted Joint Mobile Edge Computing and Data Collection via Matching-enabled Deep Reinforcement Learning
Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) and data collection (DC) have been popular research issues.
Federated Self-supervised Domain Generalization for Label-efficient Polyp Segmentation
In this paper, a Federated self-supervised Domain Generalization method is proposed to enhance the generalization capacity of federated and Label-efficient intestinal polyp segmentation, named LFDG.
Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning
Specifically, this algorithm introduces an evolutionary learning approach to obtain the approximate Pareto set for the formulated MOP.
Deep Reinforcement Learning
Multi-Objective Reinforcement Learning
Generative AI for Lyapunov Optimization Theory in UAV-based Low-Altitude Economy Networking
Extending beyond content generation, in this paper, we propose a framework integrating generative diffusion models with reinforcement learning to address Lyapunov optimization problems in UAV-based LAE networking.
Model-based Reinforcement Learning
reinforcement-learning
+2
Secure Resource Allocation via Constrained Deep Reinforcement Learning
The proliferation of Internet of Things (IoT) devices and the advent of 6G technologies have introduced computationally intensive tasks that often surpass the processing capabilities of user devices.
Task Delay and Energy Consumption Minimization for Low-altitude MEC via Evolutionary Multi-objective Deep Reinforcement Learning
The low-altitude economy (LAE), driven by unmanned aerial vehicles (UAVs) and other aircraft, has revolutionized fields such as transportation, agriculture, and environmental monitoring.
Preventing Non-intrusive Load Monitoring Privacy Invasion: A Precise Adversarial Attack Scheme for Networked Smart Meters
The scheme effectively prevents NILM models from violating appliance-level privacy, while also ensuring accurate billing calculation for users.
UAV Virtual Antenna Array Deployment for Uplink Interference Mitigation in Data Collection Networks
Thus, by jointly optimizing the excitation current weights and hover position of UAVs as well as the sequence of data transmission to various BSs, we formulate an uplink interference mitigation multi-objective optimization problem (MOOP) to decrease interference affection, enhance transmission efficiency, and improve energy efficiency, simultaneously.
RIGI: Rectifying Image-to-3D Generation Inconsistency via Uncertainty-aware Learning
Given a single image of a target object, image-to-3D generation aims to reconstruct its texture and geometric shape.
SMILE-UHURA Challenge -- Small Vessel Segmentation at Mesoscopic Scale from Ultra-High Resolution 7T Magnetic Resonance Angiograms
The human brain receives nutrients and oxygen through an intricate network of blood vessels.
DNN Task Assignment in UAV Networks: A Generative AI Enhanced Multi-Agent Reinforcement Learning Approach
Unmanned Aerial Vehicles (UAVs) possess high mobility and flexible deployment capabilities, prompting the development of UAVs for various application scenarios within the Internet of Things (IoT).
Generative AI for Data Augmentation in Wireless Networks: Analysis, Applications, and Case Study
This article systemically explores the potential and effectiveness of GenAI-driven data augmentation in wireless networks.
Digital Twin-Assisted Space-Air-Ground Integrated Multi-Access Edge Computing for Low-Altitude Economy: An Online Decentralized Optimization Approach
The emergence of space-air-ground integrated multi-access edge computing (SAGIMEC) networks opens a significant opportunity for the rapidly growing low altitude economy (LAE), facilitating the development of various applications by offering efficient communication and computing services.
SynStitch: a Self-Supervised Learning Network for Ultrasound Image Stitching Using Synthetic Training Pairs and Indirect Supervision
ISM then utilizes this synthetic paired data to learn 2DUS stitching in a supervised manner.
Hunyuan3D 1.0: A Unified Framework for Text-to-3D and Image-to-3D Generation
To address this issue, we propose a two-stage approach named Hunyuan3D 1. 0 including a lite version and a standard version, that both support text- and image-conditioned generation.
Fidelity-Imposed Displacement Editing for the Learn2Reg 2024 SHG-BF Challenge
Co-examination of second-harmonic generation (SHG) and bright-field (BF) microscopy enables the differentiation of tissue components and collagen fibers, aiding the analysis of human breast and pancreatic cancer tissues.
DRL Optimization Trajectory Generation via Wireless Network Intent-Guided Diffusion Models for Optimizing Resource Allocation
This model can be generated and fine-tuned in real time to achieve the objective and meet the constraints of target intent networks, significantly reducing state information exposure during wireless communication.
AdaptDiff: Cross-Modality Domain Adaptation via Weak Conditional Semantic Diffusion for Retinal Vessel Segmentation
Subsequently, we sample on the target domain with binary vessel masks from the source domain to get paired data, i. e., target domain synthetic images conditioned on the binary vessel map.
Signal Detection in Near-field Communication with Unknown Noise Characteristics: A Diffusion Model Method
Then, we proposed a Maximum Likelihood Estimation Diffusion Detector (MLEDD) aiming at learning the distribution of unknown noise.
Generative AI based Secure Wireless Sensing for ISAC Networks
Specifically, we first propose a discrete conditional diffusion model to generate graphs with nodes and edges, guiding the ISAC system to appropriately activate wireless links and nodes, which ensures the sensing performance while minimizing the operation cost.
PRISM Lite: A lightweight model for interactive 3D placenta segmentation in ultrasound
The proposed model adopts the segmentation from our fully automated model for initialization and is designed in a human-in-the-loop manner to achieve iterative improvements.
Personalizing Federated Instrument Segmentation with Visual Trait Priors in Robotic Surgery
To preserve the unique appearance representation of each site and gradually leverage the inter-site difference, APE introduces appearance regulation and provides customized layer-wise aggregation solutions via hypernetworks for each site's personalized parameters.
Retinal IPA: Iterative KeyPoints Alignment for Multimodal Retinal Imaging
We propose a novel framework for retinal feature point alignment, designed for learning cross-modality features to enhance matching and registration across multi-modality retinal images.
Interactive Segmentation Model for Placenta Segmentation from 3D Ultrasound images
These models produce a segmentation from visual prompts provided to indicate the target region, which may offer a feasible solution for practical use.
Predicting fluorescent labels in label-free microscopy images with pix2pix and adaptive loss in Light My Cells challenge
Recently, in silico labeling has emerged as a promising alternative, aiming to use machine learning models to directly predict the fluorescently labeled images from label-free microscopy.
Optimizing 6G Integrated Sensing and Communications (ISAC) via Expert Networks
Integrated Sensing and Communications (ISAC) is one of the core technologies of 6G, which combines sensing and communication functions into a single system.
Cyclical Weight Consolidation: Towards Solving Catastrophic Forgetting in Serial Federated Learning
In this paper, to overcome fluctuation and low efficiency in the iterative learning and forgetting process, we introduce cyclical weight consolidation (CWC), a straightforward yet potent approach specifically tailored for serial FL.
Generative Artificial Intelligence Assisted Wireless Sensing: Human Flow Detection in Practical Communication Environments
Finally, through clustering, G-HFD determines the number of subflows and the number of targets in each subflow, i. e., the subflow size.
Multi-attention Associate Prediction Network for Visual Tracking
They are capable of fully capturing the category-related semantics for classification and the local spatial contexts for regression, respectively.
Unified Diffusion-Based Rigid and Non-Rigid Editing with Text and Image Guidance
Existing text-to-image editing methods tend to excel either in rigid or non-rigid editing but encounter challenges when combining both, resulting in misaligned outputs with the provided text prompts.
Acceleration Estimation of Signal Propagation Path Length Changes for Wireless Sensing
Furthermore, in multi-target scenarios, the fall detection achieves an average true positive rate of 89. 56% and a false positive rate of 11. 78%, demonstrating its importance in enhancing indoor wireless sensing capabilities.
Generative AI for Physical Layer Communications: A Survey
The recent evolution of generative artificial intelligence (GAI) leads to the emergence of groundbreaking applications such as ChatGPT, which not only enhances the efficiency of digital content production, such as text, audio, video, or even network traffic data, but also enriches its diversity.
Novel OCT mosaicking pipeline with Feature- and Pixel-based registration
High-resolution Optical Coherence Tomography (OCT) images are crucial for ophthalmology studies but are limited by their relatively narrow field of view (FoV).
Assessing Test-time Variability for Interactive 3D Medical Image Segmentation with Diverse Point Prompts
In this paper, we assess the test-time variability for interactive medical image segmentation with diverse point prompts.
Promise:Prompt-driven 3D Medical Image Segmentation Using Pretrained Image Foundation Models
To address prevalent issues in medical imaging, such as data acquisition challenges and label availability, transfer learning from natural to medical image domains serves as a viable strategy to produce reliable segmentation results.
Generative AI for Integrated Sensing and Communication: Insights from the Physical Layer Perspective
In this article, we investigate applications of GAI in the physical layer and analyze its support for integrated sensing and communications (ISAC) systems.
CATS v2: Hybrid encoders for robust medical segmentation
We fuse the information from the convolutional encoder and the transformer at the skip connections of different resolutions to form the final segmentation.
Unleashing the Strengths of Unlabeled Data in Pan-cancer Abdominal Organ Quantification: the FLARE22 Challenge
The best-performing algorithms successfully generalized to holdout external validation sets, achieving a median DSC of 89. 5\%, 90. 9\%, and 88. 3\% on North American, European, and Asian cohorts, respectively.
Beyond Reality: The Pivotal Role of Generative AI in the Metaverse
This paper offers a comprehensive exploration of how generative AI technologies are shaping the Metaverse, transforming it into a dynamic, immersive, and interactive virtual world.
VesselMorph: Domain-Generalized Retinal Vessel Segmentation via Shape-Aware Representation
We map the intensity image and the tensor field to a latent space for feature extraction.
Deep Angiogram: Trivializing Retinal Vessel Segmentation
The generalizability of the synthetic network is improved by the contrastive loss that makes the model less sensitive to variations of image contrast and noisy features.
Democratizing Pathological Image Segmentation with Lay Annotators via Molecular-empowered Learning
The contribution of this paper is threefold: (1) We proposed a molecular-empowered learning scheme for multi-class cell segmentation using partial labels from lay annotators; (2) The proposed method integrated Giga-pixel level molecular-morphology cross-modality registration, molecular-informed annotation, and molecular-oriented segmentation model, so as to achieve significantly superior performance via 3 lay annotators as compared with 2 experienced pathologists; (3) A deep corrective learning (learning with imperfect label) method is proposed to further improve the segmentation performance using partially annotated noisy data.
Text-guided Eyeglasses Manipulation with Spatial Constraints
To address these limitations, we propose a Text-guided Eyeglasses Manipulation method that allows for control of the eyeglasses shape and style based on a binary mask and text, respectively.
Why is the winner the best?
The "typical" lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning.
Guiding AI-Generated Digital Content with Wireless Perception
Experiments results verify the effectiveness of the WP-AIGC framework, highlighting its potential as a novel approach for guiding AI models in the accurate generation of digital content.
Self-Supervised CSF Inpainting with Synthetic Atrophy for Improved Accuracy Validation of Cortical Surface Analyses
In this paper, we present a solution using a self-supervised inpainting model to generate CSF in these regions and create images with more plausible GM/CSF boundaries.
SSL^2: Self-Supervised Learning meets Semi-Supervised Learning: Multiple Sclerosis Segmentation in 7T-MRI from large-scale 3T-MRI
A potential solution is to leverage the information available in large public datasets in conjunction with a target dataset which only has limited labeled data.
AI-Generated Incentive Mechanism and Full-Duplex Semantic Communications for Information Sharing
Specifically, a user can transmit the generated content and semantic information extracted from their view image to nearby users, who can then use this information to obtain the spatial matching of computation results under their view images.
Indescribable Multi-modal Spatial Evaluator
IMSE creates an accurate multi-modal spatial evaluator to measure spatial differences between two images, and then optimizes registration by minimizing the error predicted of the evaluator.
Biomedical image analysis competitions: The state of current participation practice
Of these, 84% were based on standard architectures.
Performance Analysis of Free-Space Information Sharing in Full-Duplex Semantic Communications
In this paper, we propose a free-space information sharing mechanism based on full-duplex device-to-device (D2D) semantic communications.
Semantic Communications for Wireless Sensing: RIS-aided Encoding and Self-supervised Decoding
To select the task-related signal spectrums for achieving efficient encoding, a semantic hash sampling method is introduced.
Wireless Sensing Data Collection and Processing for Metaverse Avatar Construction
We observe that the collected sensing data, i. e., channel state information (CSI), suffers from a phase shift problem, which negatively affects the extraction of user information such as behavior and heartbeat and further deteriorates the avatar construction.
Semantic-Aware Sensing Information Transmission for Metaverse: A Contest Theoretic Approach
Inspired by emerging semantic communication, in this paper, we propose a semantic transmission framework for transmitting sensing information from the physical world to Metaverse.
Dual Multi-scale Mean Teacher Network for Semi-supervised Infection Segmentation in Chest CT Volume for COVID-19
Automated detecting lung infections from computed tomography (CT) data plays an important role for combating COVID-19.
Cats: Complementary CNN and Transformer Encoders for Segmentation
We fuse the information from the convolutional encoder and the transformer, and pass it to the decoder to obtain the results.
Exploring Attention-Aware Network Resource Allocation for Customized Metaverse Services
With the help of UOAL, we propose an attention-aware network resource allocation algorithm that has two steps, i. e., attention prediction and QoE maximization.
Personalizing Federated Medical Image Segmentation via Local Calibration
Personalized FL tackles this issue by only utilizing partial model parameters shared from global server, while keeping the rest to adapt to its own data distribution in the local training of each site.
XBound-Former: Toward Cross-scale Boundary Modeling in Transformers
Skin lesion segmentation from dermoscopy images is of great significance in the quantitative analysis of skin cancers, which is yet challenging even for dermatologists due to the inherent issues, i. e., considerable size, shape and color variation, and ambiguous boundaries.
CholecTriplet2021: A benchmark challenge for surgical action triplet recognition
In this paper, we present the challenge setup and assessment of the state-of-the-art deep learning methods proposed by the participants during the challenge.
Ranked #1 on
Action Triplet Recognition
on CholecT50 (Challenge)
(using extra training data)
ModDrop++: A Dynamic Filter Network with Intra-subject Co-training for Multiple Sclerosis Lesion Segmentation with Missing Modalities
Previously, a training strategy termed Modality Dropout (ModDrop) has been applied to MS lesion segmentation to achieve the state-of-the-art performance with missing modality.
Separated Contrastive Learning for Organ-at-Risk and Gross-Tumor-Volume Segmentation with Limited Annotation
The SIS is proposed to operate on the image set to rebuild a region set under the guidance of structural information.
Real-time landmark detection for precise endoscopic submucosal dissection via shape-aware relation network
Compared with existing solutions, which either neglect geometric relationships among targeting objects or capture the relationships by using complicated aggregation schemes, the proposed network is capable of achieving satisfactory accuracy while maintaining real-time performance by taking full advantage of the spatial relations among landmarks.
2020 CATARACTS Semantic Segmentation Challenge
In 2020, we released pixel-wise semantic annotations for anatomy and instruments for 4670 images sampled from 25 videos of the CATARACTS training set.
Boundary-aware Transformers for Skin Lesion Segmentation
Skin lesion segmentation from dermoscopy images is of great importance for improving the quantitative analysis of skin cancer.
Ranked #5 on
Lesion Segmentation
on ISIC 2018
Efficient Global-Local Memory for Real-time Instrument Segmentation of Robotic Surgical Video
On the other hand, we develop an active global memory to gather the global semantic correlation in long temporal range to current one, in which we gather the most informative frames derived from model uncertainty and frame similarity.
Atrial Fibrillation Detection and ECG Classification based on CNN-BiLSTM
It is challenging to visually detect heart disease from the electrocardiographic (ECG) signals.
Meta-Learning for Natural Language Understanding under Continual Learning Framework
Neural network has been recognized with its accomplishments on tackling various natural language understanding (NLU) tasks.
Self-semi-supervised Learning to Learn from NoisyLabeled Data
The remarkable success of today's deep neural networks highly depends on a massive number of correctly labeled data.
Robust Medical Instrument Segmentation Challenge 2019
The validation of the competing methods for the three tasks (binary segmentation, multi-instance detection and multi-instance segmentation) was performed in three different stages with an increasing domain gap between the training and the test data.
Neyman-Pearson classification: parametrics and sample size requirement
In this work, we employ the parametric linear discriminant analysis (LDA) model and propose a new parametric thresholding algorithm, which does not need the minimum sample size requirements on class $0$ observations and thus is suitable for small sample applications such as rare disease diagnosis.
