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Chapter and Conference Paper
Fault Diagnosis of Rolling Bearings Based on Neural Networks and Decision Trees
Based on the excellent accuracy of Convolutional Neural Network (CNN) models but their long algorithm runtime, and the high efficiency but relatively lower accuracy of decision tree models, this paper proposes...
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Chapter and Conference Paper
A Micro-vibration Test Method for Satellite Based on Dual-Stage Gravity Compensation System
Optical remote sensing satellites are generally composed of two parts which are satellite platform and camera. In order to evaluate the satellites on-orbit working status, it is necessary to carry out micro-vi...
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Chapter and Conference Paper
You’ve Got Two Teachers: Co-evolutionary Image and Report Distillation for Semi-supervised Anatomical Abnormality Detection in Chest X-Ray
Chest X-ray (CXR) anatomical abnormality detection aims at localizing and characterising cardiopulmonary radiological findings in the radiographs, which can expedite clinical workflow and reduce observational ...
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Chapter and Conference Paper
A Model-Agnostic Framework for Universal Anomaly Detection of Multi-organ and Multi-modal Images
The recent success of deep learning relies heavily on the large amount of labeled data. However, acquiring manually annotated symptomatic medical images is notoriously time-consuming and laborious, especially ...
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Chapter and Conference Paper
Trailer Tag Hitch: An Automatic Reverse Hanging System Using Fiducial Markers
Unmanned tractor-trailer vehicles are widely used in factory transportation scenarios. However, the trailer hitching process is still manually operated. The automatic trailer hitching is the precondition of fu...
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Chapter and Conference Paper
Personalized HRIR Based on PointNet Network Using Anthropometric Parameters
A novel deep neural network model was proposed to reconstruct the head-related impulse response (HRIR) by using three dimensional anthropometric parameters. Aiming at the point physiological parameters in the ...
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Chapter and Conference Paper
Rolling Force Prediction Based on PELM
In the process of hot strip rolling, the calculation accuracy of rolling force directly affects the actual thickness of strip steel, which is also the prerequisite of accurate online control. However, because ...
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Chapter and Conference Paper
Category-Level Regularized Unlabeled-to-Labeled Learning for Semi-supervised Prostate Segmentation with Multi-site Unlabeled Data
Segmenting prostate from MRI is crucial for diagnosis and treatment planning of prostate cancer. Given the scarcity of labeled data in medical imaging, semi-supervised learning (SSL) presents an attractive opt...
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Chapter
Gene Therapy and Cardiovascular Diseases
Cardiovascular diseases (CVDs) are the leading causes of death globally and urgently require new novel therapeutic strategies. Gene therapy is the application of gene modulation technology to treat abnormal ge...
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Chapter and Conference Paper
Towards Expert-Amateur Collaboration: Prototypical Label Isolation Learning for Left Atrium Segmentation with Mixed-Quality Labels
Deep learning-based medical image segmentation usually requires abundant high-quality labeled data from experts, yet, it is often infeasible in clinical practice. Without sufficient expert-examined labels, the...
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Chapter and Conference Paper
A Depth-Guided Attention Strategy for Crowd Counting
Crowd counting, an essential technology with numerous applications, often encounters challenges due to non-uniform crowd distributions and noisy backgrounds in congested scenes. To address these issues, this p...
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Chapter and Conference Paper
Towards Interactive Facial Image Inpainting by Text or Exemplar Image
Facial image inpainting aims to fill visually realistic and semantically new pixels for masked or missing pixels in a face image. Although current methods have made progress in achieving high visual quality, t...
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Chapter and Conference Paper
Tourism Research on National Parks and Protected Areas
By visualizing bibliometric data, this work tries to describe the scholarly landscape of the tourism research field on national parks and protected areas. Data of 930 specific documents published between 1996 ...
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Chapter and Conference Paper
Deformer: Towards Displacement Field Learning for Unsupervised Medical Image Registration
Recently, deep-learning-based approaches have been widely studied for deformable image registration task. However, most efforts directly map the composite image representation to spatial transformation through...
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Chapter and Conference Paper
An Inclusive Task-Aware Framework for Radiology Report Generation
To avoid the tedious and laborious radiology report writing, the automatic generation of radiology reports has drawn great attention recently. Previous studies attempted to directly transfer the image captioni...
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Chapter and Conference Paper
Multiscale Unsupervised Retinal Edema Area Segmentation in OCT Images
Retinal edema area, which can be observed in the non-invasive optical coherence tomography image, is essential for the diagnosis and treatment of many retinal diseases. Due to the demand of professional knowle...
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Chapter and Conference Paper
Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for Few-Shot Segmentation
Research into Few-shot Semantic Segmentation (FSS) has attracted great attention, with the goal to segment target objects in a query image given only a few annotated support images of the target class. A key t...
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Chapter and Conference Paper
Noncontact Clearance Measurement Research Based on Machine Vision
Parts assembly clearance measurement is facing a trend towards high-precision and noncontact. This work aims to measure clearance by image processing based on machine vision. The machine vision system is to hi...
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Chapter and Conference Paper
Research on Garbage Classification Based on Deep Learning
Many cities in our country are facing serious problems of garbage classification, with the rapid development of artificial intelligence and deep learning related technologies, it can provide a good and effecti...
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Chapter and Conference Paper
Denoising for Relaxing: Unsupervised Domain Adaptive Fundus Image Segmentation Without Source Data
Recently, unsupervised domain adaptation (UDA) has been actively explored for multi-site fundus image segmentation with domain discrepancy. Despite relaxing the requirement of target labels, typical UDA still ...
