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Found 43 papers, 20 papers with code
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.
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.
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.
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.
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 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.
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.
