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Found 27 papers, 12 papers with code
Devising a Set of Compact and Explainable Spoken Language Feature for Screening Alzheimer's Disease
Alzheimer's disease (AD) has become one of the most significant health challenges in an aging society.
Single-Point Supervised High-Resolution Dynamic Network for Infrared Small Target Detection
It balances network depth and feature resolution to maintain deep IR small-target information.
STS MICCAI 2023 Challenge: Grand challenge on 2D and 3D semi-supervised tooth segmentation
The Semi-supervised Teeth Segmentation (STS) Challenge, a pioneering event in tooth segmentation, was held as a part of the MICCAI 2023 ToothFairy Workshop on the Alibaba Tianchi platform.
Multi-Level Feature Fusion Network for Lightweight Stereo Image Super-Resolution
Stereo image super-resolution utilizes the cross-view complementary information brought by the disparity effect of left and right perspective images to reconstruct higher-quality images.
NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results
This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results.
Enhancing Policy Gradient with the Polyak Step-Size Adaption
Policy gradient is a widely utilized and foundational algorithm in the field of reinforcement learning (RL).
Prior Frequency Guided Diffusion Model for Limited Angle (LA)-CBCT Reconstruction
PFGDM-B, on the other hand, continuously applies the prior CT information condition in every reconstruction step, while with a decaying mechanism, to gradually phase out the reconstruction guidance from the prior CT scans.
Generalized Policy Learning for Smart Grids: FL TRPO Approach
The smart grid domain requires bolstering the capabilities of existing energy management systems; Federated Learning (FL) aligns with this goal as it demonstrates a remarkable ability to train models on heterogeneous datasets while maintaining data privacy, making it suitable for smart grid applications, which often involve disparate data distributions and interdependencies among features that hinder the suitability of linear models.
FDDM: Unsupervised Medical Image Translation with a Frequency-Decoupled Diffusion Model
This anatomical information then guides a subsequent diffusion model to generate high-quality CT images.
FDNet: Feature Decoupled Segmentation Network for Tooth CBCT Image
Precise Tooth Cone Beam Computed Tomography (CBCT) image segmentation is crucial for orthodontic treatment planning.
nnSAM: Plug-and-play Segment Anything Model Improves nnUNet Performance
To combine the strengths of foundational and domain-specific models, we propose nnSAM, integrating SAM's robust feature extraction with nnUNet's automatic configuration to enhance segmentation accuracy on small datasets.
Enhancing Point Annotations with Superpixel and Confidence Learning Guided for Improving Semi-Supervised OCT Fluid Segmentation
Specifically, we use points to annotate fluid regions in unlabeled OCT images and the Superpixel-Guided Pseudo-Label Generation (SGPLG) module generates pseudo-labels and pixel-level label trust maps from the point annotations.
SAMScore: A Semantic Structural Similarity Metric for Image Translation Evaluation
Image translation has wide applications, such as style transfer and modality conversion, usually aiming to generate images having both high degrees of realism and faithfulness.
Zero-shot Medical Image Translation via Frequency-Guided Diffusion Models
However, for medical image translation, the existing diffusion models are deficient in accurately retaining structural information since the structure details of source domain images are lost during the forward diffusion process and cannot be fully recovered through learned reverse diffusion, while the integrity of anatomical structures is extremely important in medical images.
ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledge
The primary aim of this research was to address the limitations observed in the medical knowledge of prevalent large language models (LLMs) such as ChatGPT, by creating a specialized language model with enhanced accuracy in medical advice.
Recurrence-free Survival Prediction under the Guidance of Automatic Gross Tumor Volume Segmentation for Head and Neck Cancers
For Head and Neck Cancers (HNC) patient management, automatic gross tumor volume (GTV) segmentation and accurate pre-treatment cancer recurrence prediction are of great importance to assist physicians in designing personalized management plans, which have the potential to improve the treatment outcome and quality of life for HNC patients.
LViT: Language meets Vision Transformer in Medical Image Segmentation
In our LViT model, medical text annotation is incorporated to compensate for the quality deficiency in image data.
Ranked #3 on
Medical Image Segmentation
on MoNuSeg
CDNet: Contrastive Disentangled Network for Fine-Grained Image Categorization of Ocular B-Scan Ultrasound
Precise and rapid categorization of images in the B-scan ultrasound modality is vital for diagnosing ocular diseases.
MultiHiertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Data
Numerical reasoning over hybrid data containing both textual and tabular content (e. g., financial reports) has recently attracted much attention in the NLP community.
Plug-and-play Shape Refinement Framework for Multi-site and Lifespan Brain Skull Stripping
For instance, the model trained on a dataset with specific imaging parameters cannot be well applied to other datasets with different imaging parameters.
Dispensed Transformer Network for Unsupervised Domain Adaptation
To mitigate this problem, a novel unsupervised domain adaptation (UDA) method named dispensed Transformer network (DTNet) is introduced in this paper.
GT U-Net: A U-Net Like Group Transformer Network for Tooth Root Segmentation
In this paper, we propose a novel end-to-end U-Net like Group Transformer Network (GT U-Net) for the tooth root segmentation.
AGMB-Transformer: Anatomy-Guided Multi-Branch Transformer Network for Automated Evaluation of Root Canal Therapy
Accurate evaluation of the treatment result on X-ray images is a significant and challenging step in root canal therapy since the incorrect interpretation of the therapy results will hamper timely follow-up which is crucial to the patients' treatment outcome.
High-Resolution Segmentation of Tooth Root Fuzzy Edge Based on Polynomial Curve Fitting with Landmark Detection
As the most economical and routine auxiliary examination in the diagnosis of root canal treatment, oral X-ray has been widely used by stomatologists.
A cascade network for Detecting COVID-19 using chest x-rays
Then we used SEME-DenseNet169 for fine-grained classification of viral pneumonia and determined if it is caused by COVID-19.
Efficient and Robust Reinforcement Learning with Uncertainty-based Value Expansion
By integrating dynamics models into model-free reinforcement learning (RL) methods, model-based value expansion (MVE) algorithms have shown a significant advantage in sample efficiency as well as value estimation.
Hyperspectral City V1.0 Dataset and Benchmark
This document introduces the background and the usage of the Hyperspectral City Dataset and the benchmark.
