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UAVs for Monitoring Property Code Violations
City offices are constantly responding to public service requests. The efficiency at which they can do this depends on gathering data about where and...
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An Overview of UAVs for Spatial Modeling and Urban Informatics
The recent growth of unmanned aerial vehicles (UAVs) for research is astounding. UAVs provide a unique platform for develo** a geospatial...
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Maximizing the Thermal Comfort of Pedestrians with UAV Imagery and Multiobjective Spatial Optimization
This chapter explores the application of Unmanned Aerial Vehicle (UAV) imagery for identifying shaded paths, which can be valuable for urban...
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Drones and Their Future Applications
Predicting the future of drones and their operational characteristics, including onboard computing, propulsion systems, and battery longevity, is...
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UAVs for Rapid Storm Damage Assessment
Nearly one-half of the annual precipitation in the Phoenix metropolitan area occurs during the North American monsoon (NAM). While these convective...
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The CHIP 2023 Shared Task 6: Chinese Diabetes Question Classification
Medical question classification is one of essential tasks in the processing of medical question data for enhancing the capability of medical...
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ECNU-LLM@CHIP-PromptCBLUE: Prompt Optimization and In-Context Learning for Chinese Medical Tasks
Our team, ECNU-LLM, presents a method of in-context learning for enhancing the performance of large language models without fine-tuning in the 9th...
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Innovative Design of Large Language Model in the Medical Field Based on chip-PromptCBLUE
This article introduces the research content and results based on the CHIP-PromptCBLUE (Chinese Biomedical Language Understanding Evaluation)...
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Chinese Diabetes Question Classification Using Large Language Models and Transfer Learning
Type 2 diabetes has evolved into a significant global public health challenge. Diabetes question-answering services are playing an increasingly...
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Chinese Biomedical NER Based on Self-attention and Word-Relation Decoding Strategy
Biomedical named entity recognition plays a crucial role in advancing smart healthcare tasks. However, the scarcity of biomedical data and the...
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Improving Hybrid Quantum Annealing Tomographic Image Reconstruction with Regularization Strategies
Quantum computing and quantum annealing present promising avenues for addressing complex problems in various fields, including tomographic image...
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Neural Implicit k-space with Trainable Periodic Activation Functions for Cardiac MR Imaging
In MRI reconstruction, neural implicit k-space (NIK) representation maps spatial frequencies to k-space intensity values using an MLP with periodic...
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Abstract: Radiomics Processing Toolkit
Radiomics focuses on extracting and analyzing quantitative features from medical images. Standardizing radiomics is difficult due to variations...
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Guidance to Noise Simulation in X-ray Imaging
In medical imaging, noise is an inherent occurring signal corruption, especially for the X-ray imaging where dose exposure to the patient should be...
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Ultrasound to CT Image-to-image Translation for Personalized Thyroid Screening
The use of 2D scintigraphy in the screening for thyroid pathologies is widespread, however its diagnostic value is limited because the activity of...
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Abstract: Cytologic Scoring of Equine Exercise-induced Pulmonary Hemorrhage
Exercise-induced pulmonary hemorrhage (EIPH) is a common respiratory condition in race horses with negative implications on performance. The gold...
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Improving Segmentation Models for AR-guided Liver Surgery using Synthetic Images
AR-guided open liver surgery is a field of intense research. However, due to the lack ofRGB-D videos of the surgery scene, there are not any...
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Abstract: Realistic Collimated X-ray Image Simulation Pipeline
Collimator detection in X-ray systems has long posed a formidable challenge, particularly when information about the detector’s position relative to...
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Deep Image Prior for Spatio-temporal Fluorescence Microscopy Images DECO-DIP
Image deconvolution and denoising is a common postprocessing step to improve the quality of biomedical fluorescence microscopy images. In recent...
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Influence of imperfect annotations on deep learning segmentation models
Convolutional neural networks are the most commonly used models for multi-organ segmentation in CT volumes. Most approaches are based on supervised...