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Found 17 papers, 9 papers with code
nnSAM: Plug-and-play Segment Anything Model Improves nnUNet Performance
The recent developments of foundation models in computer vision, especially the Segment Anything Model (SAM), allow scalable and domain-agnostic image segmentation to serve as a general-purpose segmentation tool.
Learning from Noisy Labels Generated by Extremely Point Annotations for OCT Fluid Segmentation
To address this issue, (i) we propose a superpixel-guided method for generating noisy labels from weak point annotations, called Point to Noisy by Superpixel (PNS), which limits the network from over-fitting noise by assigning low confidence to suspiciously noisy label pixels, and (ii) we propose a Two-Stage Mean-Teacher-assisted Confident Learning (2SMTCL) method based on MTCL for multi-category OCT fluid segmentation, which alleviates the uncertainty and computing power consumption introduced by the real-time characterization noise of MTCL.
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 #1 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.
