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Found 16 papers, 5 papers with code
Source-free Domain Adaptation for Multi-site and Lifespan Brain Skull Stripping
In this paper, we design a source-free domain adaptation framework (SDAF) for multi-site and lifespan skull stripping that can accomplish domain adaptation without access to source domain images.
Semantic Similarity Computing Model Based on Multi Model Fine-Grained Nonlinear Fusion
Experimental results show that the matching of sentence similarity calculation method based on multi model nonlinear fusion is 84%, and the F1 value of the model is 75%.
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.
3D Object Detection Combining Semantic and Geometric Features from Point Clouds
The SGNet proposed in this paper has achieved state-of-the-art results for 3D object detection in the KITTI dataset, especially in the detection of small-size objects such as cyclists.
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.
Structure-aware scale-adaptive networks for cancer segmentation in whole-slide images
Cancer segmentation in whole-slide images is a fundamental step for viable tumour burden estimation, which is of great value for cancer assessment.
Magnification-independent Histopathological Image Classification with Similarity-based Multi-scale Embeddings
Experimental results show that the SMSE improves the performance for histopathological image classification tasks for both breast and liver cancers by a large margin compared to previous methods.
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.
Multiscale Attention Guided Network for COVID-19 Diagnosis Using Chest X-ray Images
Moreover, massive data collection is impractical for a newly emerged disease, which limited the performance of data thirsty deep learning models.
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.
An Adaptive Enhancement Based Hybrid CNN Model for Digital Dental X-ray Positions Classification
In this project, a novel solution based on adaptive histogram equalization and convolution neural network (CNN) is proposed, which automatically performs the task for dental x-rays.
DDU-Nets: Distributed Dense Model for 3D MRI Brain Tumor Segmentation
Segmentation of brain tumors and their subregions remains a challenging task due to their weak features and deformable shapes.
BACH: Grand Challenge on Breast Cancer Histology Images
From the submitted algorithms it was possible to push forward the state-of-the-art in terms of accuracy (87%) in automatic classification of breast cancer with histopathological images.
