Search Results for author:
Found 36 papers, 12 papers with code
SemiGNN-PPI: Self-Ensembling Multi-Graph Neural Network for Efficient and Generalizable Protein-Protein Interaction Prediction
Protein-protein interactions (PPIs) are crucial in various biological processes and their study has significant implications for drug development and disease diagnosis.
Meta-hallucinator: Towards Few-Shot Cross-Modality Cardiac Image Segmentation
To achieve efficient few-shot cross-modality segmentation, we propose a novel transformation-consistent meta-hallucination framework, meta-hallucinator, with the goal of learning to diversify data distributions and generate useful examples for enhancing cross-modality performance.
LE-UDA: Label-efficient unsupervised domain adaptation for medical image segmentation
To assess the effectiveness of our method, we conduct extensive experiments on two different tasks for cross-modality segmentation between MRI and CT images.
MMGL: Multi-Scale Multi-View Global-Local Contrastive learning for Semi-supervised Cardiac Image Segmentation
With large-scale well-labeled datasets, deep learning has shown significant success in medical image segmentation.
ACT-Net: Asymmetric Co-Teacher Network for Semi-supervised Memory-efficient Medical Image Segmentation
While deep models have shown promising performance in medical image segmentation, they heavily rely on a large amount of well-annotated data, which is difficult to access, especially in clinical practice.
Residual Channel Attention Network for Brain Glioma Segmentation
A glioma is a malignant brain tumor that seriously affects cognitive functions and lowers patients' life quality.
Deep Feature Fusion via Graph Convolutional Network for Intracranial Artery Labeling
Intracranial arteries are critical blood vessels that supply the brain with oxygenated blood.
Object-Aware Self-supervised Multi-Label Learning
Multi-label Learning on Image data has been widely exploited with deep learning models.
Self-supervised Assisted Active Learning for Skin Lesion Segmentation
Label scarcity has been a long-standing issue for biomedical image segmentation, due to high annotation costs and professional requirements.
Adaptive Mean-Residue Loss for Robust Facial Age Estimation
Automated facial age estimation has diverse real-world applications in multimedia analysis, e. g., video surveillance, and human-computer interaction.
MT-UDA: Towards Unsupervised Cross-modality Medical Image Segmentation with Limited Source Labels
Although deep unsupervised domain adaptation (UDA) can leverage well-established source domain annotations and abundant target domain data to facilitate cross-modality image segmentation and also mitigate the label paucity problem on the target domain, the conventional UDA methods suffer from severe performance degradation when source domain annotations are scarce.
Multi-Slice Dense-Sparse Learning for Efficient Liver and Tumor Segmentation
Accurate automatic liver and tumor segmentation plays a vital role in treatment planning and disease monitoring.
Automatic Liver And Tumor Segmentation
Image Segmentation
+2
Two Eyes Are Better Than One: Exploiting Binocular Correlation for Diabetic Retinopathy Severity Grading
Diabetic retinopathy (DR) is one of the most common eye conditions among diabetic patients.
Hierarchical Consistency Regularized Mean Teacher for Semi-supervised 3D Left Atrium Segmentation
Deep learning has achieved promising segmentation performance on 3D left atrium MR images.
An Automated Machine Learning (AutoML) Method for Driving Distraction Detection Based on Lane-Keeping Performance
An AutoML method based on XGBoost, termed AutoGBM, is built as the classifier for prediction and feature ranking.
DSAL: Deeply Supervised Active Learning from Strong and Weak Labelers for Biomedical Image Segmentation
We use the proposed criteria to select samples for strong and weak labelers to produce oracle labels and pseudo labels simultaneously at each active learning iteration in an ensemble learning manner, which can be examined with IoMT Platform.
Dynamic Planning of Bicycle Stations in Dockless Public Bicycle-sharing System Using Gated Graph Neural Network
The BSDP system contains four modules: bicycle drop-off location clustering, bicycle-station graph modeling, bicycle-station location prediction, and bicycle-station layout recommendation.
Dynamic Bicycle Dispatching of Dockless Public Bicycle-sharing Systems using Multi-objective Reinforcement Learning
We model the DL-PBS system from the perspective of CPS and use deep learning to predict the layout of bicycle parking spots and the dynamic demand of bicycle dispatching.
Automatic Clustering for Unsupervised Risk Diagnosis of Vehicle Driving for Smart Road
Secondly, we propose balanced Silhouette Index (bSI) to evaluate the internal quality of imbalanced clustering.
Sea-Net: Squeeze-And-Excitation Attention Net For Diabetic Retinopathy Grading
Diabetes is one of the most common disease in individuals.
A Hierarchical Deep Convolutional Neural Network and Gated Recurrent Unit Framework for Structural Damage Detection
Specifically, CNN is utilized to model the spatial relations and the short-term temporal dependencies among sensors, while the output features of CNN are fed into the GRU to learn the long-term temporal dependencies jointly.
Multi-Instance Multi-Label Learning for Gene Mutation Prediction in Hepatocellular Carcinoma
Gene mutation prediction in hepatocellular carcinoma (HCC) is of great diagnostic and prognostic value for personalized treatments and precision medicine.
Multi-Phase Cross-modal Learning for Noninvasive Gene Mutation Prediction in Hepatocellular Carcinoma
Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and the fourth most common cause of cancer-related death worldwide.
Deeply Supervised Active Learning for Finger Bones Segmentation
Segmentation is a prerequisite yet challenging task for medical image analysis.
Multi-Target Deep Learning for Algal Detection and Classification
Water quality has a direct impact on industry, agriculture, and public health.
Ordered or Orderless: A Revisit for Video based Person Re-Identification
Specifically, with a diagnostic analysis, we show that the recurrent structure may not be effective to learn temporal dependencies than what we expected and implicitly yields an orderless representation.
FaultNet: Faulty Rail-Valves Detection using Deep Learning and Computer Vision
In this paper, a multi-phase deep learning based technique is proposed to perform accurate fault detection of rail-valves.
Cribriform pattern detection in prostate histopathological images using deep learning models
In this paper, we present an annotated cribriform dataset along with analysis of deep learning models and hand-crafted features for cribriform pattern detection in prostate histopathological images.
Robust Regression via Deep Negative Correlation Learning
Nonlinear regression has been extensively employed in many computer vision problems (e. g., crowd counting, age estimation, affective computing).
A Deep Framework for Bone Age Assessment based on Finger Joint Localization
In this study, we applied powerful deep neural network and explored a process in the forecast of skeletal bone age with the specifically combine joints images to increase the performance accuracy compared with the whole hand images.
Semi-Supervised Self-Taught Deep Learning for Finger Bones Segmentation
Segmentation stands at the forefront of many high-level vision tasks.
Graph Convolutional Neural Networks for Polymers Property Prediction
A fast and accurate predictive tool for polymer properties is demanding and will pave the way to iterative inverse design.
End-to-End Video Classification with Knowledge Graphs
In particular, we unify traditional "knowledgeless" machine learning models and knowledge graphs in a novel end-to-end framework.
Truly Multi-modal YouTube-8M Video Classification with Video, Audio, and Text
Beyond that, we extend the original competition by including text information in the classification, making this a truly multi-modal approach with vision, audio and text.
Deep Learning for Lung Cancer Detection: Tackling the Kaggle Data Science Bowl 2017 Challenge
We present a deep learning framework for computer-aided lung cancer diagnosis.
A Novel Multi-task Deep Learning Model for Skin Lesion Segmentation and Classification
In this study, a multi-task deep neural network is proposed for skin lesion analysis.
