Thyroid disorders are a complex group of diseases that require an accurate diagnosis for effective treatment. Fine-needle aspiration biopsies can assist in detecting many thyroid diseases. These materials can be analy...
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ISBN:
(纸本)9798350312249
Thyroid disorders are a complex group of diseases that require an accurate diagnosis for effective treatment. Fine-needle aspiration biopsies can assist in detecting many thyroid diseases. These materials can be analyzed visually using traditional computer vision methods, despite the limitations of complex samples. To address this problem, we propose a novel approach that uses hyperspectral imaging (HSI) to analyze thyroid biological samples. HSI measures the absorbance of infrared light by biological samples using a micro Fourier transform infrared spectroscopy (micro-FTIR) and converts this data into hyperspectral images. In this study, we used HSI to train and validate a recurrent neural network to classify thyroid samples as healthy, cancerous, or goiter. Our experiments, based on the k-fold cross-validation, achieved an overall accuracy of 96.88%, a sensitivity of 96.87%, and a specificity of 98.45%. These results demonstrate the potential of hyperspectral imaging as a tool to assist pathologists in the diagnosis of thyroid disease.
The proceedings contain 38 papers. The special focus in this conference is on Automated Technology for Verification and Analysis. The topics include: Model Checking Strategies from Synthesis over Finite Traces;re...
ISBN:
(纸本)9783031453311
The proceedings contain 38 papers. The special focus in this conference is on Automated Technology for Verification and Analysis. The topics include: Model Checking Strategies from Synthesis over Finite Traces;reactive Synthesis of Smart Contract Control Flows;synthesis of distributed Protocols by Enumeration Modulo Isomorphisms;controller Synthesis for Reactive systems with Communication Delay by Formula Translation;statistical Approach to Efficient and Deterministic Schedule Synthesis for Cyber-Physical systems;compositional High-Quality Synthesis;learning Provably Stabilizing Neural Controllers for Discrete-Time Stochastic systems;an Automata-Theoretic Approach to Synthesizing Binarized Neural networks;syntactic vs Semantic Linear Abstraction and Refinement of Neural networks;learning Nonlinear Hybrid Automata from Input–Output Time-Series Data;using Counterexamples to Improve Robustness Verification in Neural networks;a Novel Family of Finite Automata for Recognizing and Learning ω -Regular Languages;on the Containment Problem for Deterministic Multicounter Machine Models;parallel and Incremental Verification of Hybrid Automata with Ray and Verse;an Automata Theoretic Characterization of Weighted First-Order Logic;Graph-Based Reductions for Parametric and Weighted MDPs;scenario Approach for Parametric Markov Models;Fast Verified SCCs for Probabilistic Model Checking.
Edge computing plays a pivotal role in IoT applications that require rapid and secure data processing. How-ever, these applications are typically resource-demanding, and the resources available at the edge are often s...
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The proceedings contain 38 papers. The special focus in this conference is on Automated Technology for Verification and Analysis. The topics include: Model Checking Strategies from Synthesis over Finite Traces;re...
ISBN:
(纸本)9783031453281
The proceedings contain 38 papers. The special focus in this conference is on Automated Technology for Verification and Analysis. The topics include: Model Checking Strategies from Synthesis over Finite Traces;reactive Synthesis of Smart Contract Control Flows;synthesis of distributed Protocols by Enumeration Modulo Isomorphisms;controller Synthesis for Reactive systems with Communication Delay by Formula Translation;statistical Approach to Efficient and Deterministic Schedule Synthesis for Cyber-Physical systems;compositional High-Quality Synthesis;learning Provably Stabilizing Neural Controllers for Discrete-Time Stochastic systems;an Automata-Theoretic Approach to Synthesizing Binarized Neural networks;syntactic vs Semantic Linear Abstraction and Refinement of Neural networks;learning Nonlinear Hybrid Automata from Input–Output Time-Series Data;using Counterexamples to Improve Robustness Verification in Neural networks;a Novel Family of Finite Automata for Recognizing and Learning ω -Regular Languages;on the Containment Problem for Deterministic Multicounter Machine Models;parallel and Incremental Verification of Hybrid Automata with Ray and Verse;an Automata Theoretic Characterization of Weighted First-Order Logic;Graph-Based Reductions for Parametric and Weighted MDPs;scenario Approach for Parametric Markov Models;Fast Verified SCCs for Probabilistic Model Checking.
In recent years, the rapid development and widespread applications of non-overlapping community discovery have led to a growing issue of privacy leakage. The core of this problem lies in the community discovery algori...
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The domain of palmprint recognition, characterized by its convenience, low privacy sensitivity, and rich feature sets, has garnered increasing research interest. Moreover, Vision Transformers (ViTs) have emerged as a ...
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Increasing diversity of applications and services are being migrated to modern data center networks (DCNs), and these applications and services are typically generating various combinations of long and short flows wit...
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The proliferation of distributed energy resources has heightened the interactions between transmission and distribution (T&D) systems, necessitating novel analyses for the reliable operation and planning of interc...
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In this work we experimentally demonstrate a wirelessly coordinated three-element coherent distributed phased array performing beamforming and beam steering to a target in the far-field over 17 m at a carrier frequenc...
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Convolutional Neural networks (CNNs) have demonstrated remarkable performance across various computer vision tasks. Due to the computational and data-intensive nature of CNNs, Field-Programmable Gate Arrays (FPGAs) ar...
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