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Found 25 papers, 13 papers with code
Hierarchical Point-based Active Learning for Semi-supervised Point Cloud Semantic Segmentation
The most recent methods of this kind measure the uncertainty of each pre-divided region for manual labelling but they suffer from redundant information and require additional efforts for region division.
Joint Dense-Point Representation for Contour-Aware Graph Segmentation
We present a novel methodology that combines graph and dense segmentation techniques by jointly learning both point and pixel contour representations, thereby leveraging the benefits of each approach.
3D Coronary Vessel Reconstruction from Bi-Plane Angiography using Graph Convolutional Networks
X-ray coronary angiography (XCA) is used to assess coronary artery disease and provides valuable information on lesion morphology and severity.
CTooth+: A Large-scale Dental Cone Beam Computed Tomography Dataset and Benchmark for Tooth Volume Segmentation
This work provides a new benchmark for the tooth volume segmentation task, and the experiment can serve as the baseline for future AI-based dental imaging research and clinical application development.
DU-Net based Unsupervised Contrastive Learning for Cancer Segmentation in Histology Images
In this paper, we introduce an unsupervised cancer segmentation framework for histology images.
CTooth: A Fully Annotated 3D Dataset and Benchmark for Tooth Volume Segmentation on Cone Beam Computed Tomography Images
Several state-of-the-art segmentation methods are evaluated on this dataset.
Complexity Reduction of Learned In-Loop Filtering in Video Coding
In video coding, in-loop filters are applied on reconstructed video frames to enhance their perceptual quality, before storing the frames for output.
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.
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.
SRPN: similarity-based region proposal networks for nuclei and cells detection in histology images
The embedding layer is added into the region proposal networks, enabling the networks to learn discriminative features based on similarity learning.
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.
Multimodal Gait Recognition for Neurodegenerative Diseases
In recent years, single modality based gait recognition has been extensively explored in the analysis of medical images or other sensory data, and it is recognised that each of the established approaches has different strengths and weaknesses.
Perceptual underwater image enhancement with deep learning and physical priors
Underwater image enhancement, as a pre-processing step to improve the accuracy of the following object detection task, has drawn considerable attention in the field of underwater navigation and ocean exploration.
Deep Learning Methods for Lung Cancer Segmentation in Whole-slide Histopathology Images -- the ACDC@LungHP Challenge 2019
All methods were based on deep learning and categorized into two groups: multi-model method and single model method.
Deep Learning Based Brain Tumor Segmentation: A Survey
Brain tumor segmentation is one of the most challenging problems in medical image analysis.
CANet: Context Aware Network for 3D Brain Glioma Segmentation
Automated segmentation of brain glioma plays an active role in diagnosis decision, progression monitoring and surgery planning.
Cost-sensitive Boosting Pruning Trees for depression detection on Twitter
Depression is one of the most common mental health disorders, and a large number of depressed people commit suicide each year.
US-net for robust and efficient nuclei instance segmentation
We present a novel neural network architecture, US-Net, for robust nuclei instance segmentation in histopathology images.
GAN-based Virtual Re-Staining: A Promising Solution for Whole Slide Image Analysis
We proposed a conditional CycleGAN (cCGAN) network to transform the H\&E stained images into IHC stained images, facilitating virtual IHC staining on the same slide.
CUNet: A Compact Unsupervised Network for Image Classification
In this paper, we propose a compact network called CUNet (compact unsupervised network) to counter the image classification challenge.
A Distributed Deep Representation Learning Model for Big Image Data Classification
The result implies that our approach is more efficient than the conventional deep learning approaches, and can be applied to big data that is too complex for parameter designing focused approaches.
NIST: An Image Classification Network to Image Semantic Retrieval
This paper proposes a classification network to image semantic retrieval (NIST) framework to counter the image retrieval challenge.
