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Data engineering and AI-Powered Skin Cancer Identification for Healthcare Applications

Procedia computer Science 2024年 246卷 179-188页

作者： Imran Ahmed Misbah Ahmad Abdellah Chehri Gwanggil Jeon School of Computing and Information Science Anglia Ruskin University Cambridge UK Animal and Agriculture Department Hartpury University Gloucester UK Centre for Machine Vision Bristol Robotics Laboratory University of the West of England Bristol UK Department of Mathematics and Computer Science at the Royal Military College of Canada (RMC) Canada Department of Embedded Systems Engineering Incheon National University Incheon Korea

Skin cancer diagnosis, a critical task in the medical domain, can be revolutionized through the application of advanced deep-learning techniques. This work investigates the efficacy of Convolutional Neural Networks (CNNs) in the automated classification of skin cancer. The process begins with a comprehensive explanation of key CNN layers: Conv2D, MaxPool2D, Dropout, and Dense. The Conv2D layers employ learnable filters that transform localized image segments, while MaxPool2D contributes to downsampling, effectively reducing computational cost and overfitting risk. Integrating these layers enables the network to capture local and global characteristics, which is crucial for accurate classification. Adding Dropout layers enhances generalization and mitigates overfitting by introducing randomness during training. ReLU activation functions infuse non-linearity, and the Flatten layer facilitates the transition to fully connected layers. The proposed CNN architecture is meticulously designed considering filter counts, kernel sizes, and pooling dimensions. The trained model demonstrates promising performance by utilizing the HAM10000 dataset, encompassing diverse skin lesion images across seven classes. The CNN model’s parameters and architecture are systematically presented, offering insights into its design rationale. The model undergoes optimization with the Adam optimizer and annealing techniques to facilitate convergence. The model’s effectiveness is evaluated on validation and test datasets, demonstrating an accuracy of 78.55% and 76.49%, respectively, for skin cancer classification. Data augmentation strategies are introduced to enhance model generalization further. The results underscore CNN’s potential as a robust tool for automating skin cancer diagnosis, aligning with the broader trend of leveraging deep learning for medical image analysis

关键词： Image Processing Medical Imaging Deep learning Skin Cancer Data Analytics

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Ultra-Compact Magnetoelectric Sensors for Femto-Tesla Vlf Signal Reception

SSRN

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SSRN 2024年

作者： Dong, Cunzheng Sun, Changxing Chen, Lei He, Yifan Liu, Yisi Luo, Bin Sun, Nian-Xiang Department of Electrical and Computer Engineering Northeastern University BostonMA02115 United States State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin300130 China Key Lab of Computer Vision and Intelligent Information System Chongqing University of Arts and Sciences Chongqing402160 China

Very low-frequency (VLF) electromagnetic waves can penetrate dense conductive media, such as earth and saltwater, with minimal attenuation, allowing for long-distance signal transmission via ionospheric reflection. These characteristics make VLF waves ideal for applications in submarine navigation, subterranean mapping, underground communication, and ionospheric remote sensing. Conventional VLF signal reception has relied on magnetic loop antennas due to their low noise performance;however, their large size and reduced sensitivity due to low quality factors (Q) limit their use in portable and compact applications, particularly in underwater and underground environments. To address these challenges, we propose an ultra-compact room-temperature extremely sensitive femto-tesla magnetic sensor based on a strain-mediated high-Q Metglas/Quartz magnetoelectric (ME) resonator operating at its electromechanical resonance (EMR) at 24.55 kHz for VLF signal reception. The Metglas/Quartz ME sensor demonstrates a sensitivity and magnetic noise performance enhancement by an order of magnitude compared to conventional Metglas/PZT ME sensors, achieving an ultra-low equivalent magnetic noise level of 5 fT/Hz1/2, owing to high magnetic permeability and magnetostriction of Metglas and the high-quality factor of Quartz at EMR. Moreover, the Metglas/Quartz ME VLF receiver exhibits overwhelming near-field and far-field VLF signal reception capability, realizing a successful reception of VLF signal 400 km away from the NAA VLF Transmitter Cutler. The demonstrated ultra-compact high-Q Metglas/Quartz ME sensor capable of femto-tesla VLF signal reception shows significant improvements in magnetic sensing capability, size, power consumption, and cost compared to traditional magnetic loop antennas, making it a promising solution for portable VLF signal reception in challenging environments. © 2024, The Authors. All rights reserved.

关键词： Loop antennas

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P1AC: Revisiting Absolute Pose From a Single Affine Correspondence

P1AC: Revisiting Absolute Pose From a Single Affine Correspo...

引用

International Conference on computer vision (ICCV)

作者： Jonathan Ventura Zuzana Kukelova Torsten Sattler Dániel Baráth Department of Computer Science & Software Engineering Cal Poly San Luis Obispo Visual Recognition Group Faculty of Electrical Engineering Czech Technical University in Prague Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Computer Vision and Geometry Group ETH Zürich

Affine correspondences have traditionally been used to improve feature matching over wide baselines. While recent work has successfully used affine correspondences to solve various relative camera pose estimation problems, less attention has been given to their use in absolute pose estimation. We introduce the first general solution to the problem of estimating the pose of a calibrated camera given a single observation of an oriented point and an affine correspondence. The advantage of our approach (P1AC) is that it requires only a single correspondence, in comparison to the traditional point-based approach (P3P), significantly reducing the combinatorics in robust estimation. P1AC provides a general solution that removes restrictive assumptions made in prior work and is applicable to large-scale image-based localization. We propose a minimal solution to the P1AC problem and evaluate our novel solver on synthetic data, showing its numerical stability and performance under various types of noise. On standard image-based localization benchmarks we show that P1AC achieves more accurate results than the widely used P3P algorithm. Code for our method is available at https://***/jonathanventura/P1AC/.

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High-Quality, Low-Quantity: A Data-Centric Approach to Deep Learning Performance Optimization in Digital X-Ray Radiography

SSRN

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SSRN 2024年

作者： Hena, Bata Wei, Ziang Castanedo, Clemente Ibarra Maldague, Xavier Department of Electrical and Computer Engineering Université Laval 1065 Ave de la Médecine Quebec CityQCG1V 0A6 Canada Computer Vision and Systems Laboratory Department of Electrical and Computer Engineering Université Laval 1065 Ave de la Médecine Quebec CityQCG1V 0A6 Canada

The accuracy of identifying defects using specialized deep learning models can be affected by the circumstances in which the training data is curated. This is especially evident in digital X-ray radiography, where the depiction of flaws is significantly impacted by the exposure conditions. This study examines the effect of curating high-quality data on deep learning models. The variation in contrast-to-noise ratio (CNR), which is a crucial metric of image quality between features of interest and an adjacent normal background, has been found to be a key factor in model generalization in digital X-ray radiography applications. By making systematic alterations to exposure conditions during data curation, it was possible to obtain several representations of flaws in each test component with varying contrast-to-noise ratios (CNR) in the resultant radiographs. To evaluate the efficacy of the model under various conditions, two distinct datasets were curated. Dataset 1 was obtained by acquiring images with a consistent exposure setting on 140 test samples. The samples contained 4 morphologically distinct classes of flat bottom holes with seven different depths and sizes. The contrast-to-noise ratio (CNR) representations of flaws in this dataset can be attributed only to differences in depth in Dataset 1. Additionally, Dataset 2 was curated with an expanded range of CNR values by methodically adjusting exposure settings during image acquisitions. Hence, only 42% of the test pieces from Dataset 1, which had three distinct depths of flat bottom holes, were used. Each of the two datasets was used to separately train YOLOv8 for instance segmentation and U-net for multi-class semantic segmentation. Each model was trained under the same conditions, and their performances were assessed using test sets from both dataset groups. The model trained on Dataset 1 exhibited a notable decline in performance when evaluated on test sets from Dataset 2, suggesting a lack of generalization ab

关键词： Image acquisition

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QUADFormer: Learning-based Detection of Cyber Attacks in Quadrotor UAVs

arXiv

引用

arXiv 2024年

作者： Wang, Pengyu Yang, Zhaohua Yang, Nachuan Wang, Zikai Li, Jialu Zhang, Fan Wang, Chaoqun Wang, Jiankun Meng, Max Q.-H. Shi, Ling Shenzhen Key Laboratory of Robotics Perception and Intelligence The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong The School of Control Science and Engineering Shandong University Shandong China Jiaxing Research Institute Southern University of Science and Technology Jiaxing China Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong Department of Electrical and Computer Engineering The University of Alberta Canada

Safety-critical intelligent cyber-physical systems, such as quadrotor unmanned aerial vehicles (UAVs), are vulnerable to different types of cyber attacks, and the absence of timely and accurate attack detection can lead to severe consequences. When UAVs are engaged in large outdoor maneuvering flights, their system constitutes highly nonlinear dynamics that include non-Gaussian noises. Therefore, the commonly employed traditional statistics-based and emerging learning-based attack detection methods do not yield satisfactory results. In response to the above challenges, we propose QUADFormer, a novel Quadrotor UAV Attack Detection framework with transFormer-based architecture. This framework includes a residue generator designed to generate a residue sequence sensitive to anomalies. Subsequently, this sequence is fed into a transformer structure with disparity in correlation to specifically learn its statistical characteristics for the purpose of classification and attack detection. Finally, we design an alert module to ensure the safe execution of tasks by UAVs under attack conditions. We conduct extensive simulations and real-world experiments, and the results show that our method has achieved superior detection performance compared with many state-of-the-art methods. (Video1) Copyright © 2024, The Authors. All rights reserved.

关键词： Antennas

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Diagnostic Accuracy and Reliability of Deep Learning-Based Human Papillomavirus Status Prediction in Oropharyngeal Cancer

Diagnostic Accuracy and Reliability of Deep Learning-Based H...

引用

International Conference on Medical Imaging and computer-Aided Diagnosis, MICAD 2022

作者： La Greca Saint-Esteven, Agustina Marchiori, Chiara Bogowicz, Marta Barranco-García, Javier Khodabakhshi, Zahra Konukoglu, Ender Riesterer, Oliver Balermpas, Panagiotis Hüllner, Martin Malossi, A. Cristiano I. Guckenberger, Matthias van Timmeren, Janita E. Tanadini-Lang, Stephanie Department of Radiation Oncology University Hospital Zurich and the University of Zurich Rämistrasse 100 Zurich8091 Switzerland Computer Vision Laboratory Department of Information Technology and Electrical Engineering ETH Zurich Sternwartstrasse 7 Zurich8092 Switzerland IBM Research Zurich Säumerstrasse 4 Rüschlikon8803 Switzerland Department of Nuclear Medicine University Hospital Zurich and the University of Zurich Rämistrasse 100 Zurich8091 Switzerland

ISBN: (纸本)9789811667749

Oropharyngeal cancer (OPC) patients with associated human papillomavirus (HPV) infection generally present more favorable outcomes than HPV-negative patients and, consequently, their treatment with radiation therapy may be potentially de-escalated. The diagnostic accuracy of a deep learning (DL) model to predict HPV status on computed tomography (CT) images was evaluated in this study, together with its ability to perform unsupervised heatmap-based localization of relevant regions in OPC and HPV infection, i.e., the primary tumor and lymph nodes, as a measure of its reliability. The dataset consisted of 767 patients from one internal and two public collections from The Cancer Imaging Archive and was split into training, validation and test sets using the ratio 60–20–20. Images were resampled to a resolution of 2 mm3 and a sub-volume of 96 pixels3 was automatically cropped, which spanned from the nose until the start of the lungs. Models Genesis was fine-tuned for the classification task. Grad-CAM and Score-CAM were applied to the test subjects that belonged to the internal cohort (n = 24), and the overlap and Dice coefficients between the resulting heatmaps and the planning target volumes (PTVs) were calculated. Final train/validation/test area-under-the-curve (AUC) values of 0.9/0.87/0.87, accuracies of 0.83/0.82/0.79, and F1-scores of 0.83/0.79/0.74 were achieved. The reliability analysis showed an increased focus on dental artifacts in HPV-positive patients, whereas promising overlaps and moderate Dice coefficients with the PTVs were obtained for HPV-negative cases. These findings prove the necessity of performing reliability studies before a DL model is implemented in a real clinical setting, even if there is optimal diagnostic accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Diseases

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Direct Shooting Method for Numerical Optimal Control: A Modified Transcription Approach

Direct Shooting Method for Numerical Optimal Control: A Modi...

引用

European Control Conference (ECC)

作者： Jiawei Tang Yuxing Zhong Pengyu Wang Xingzhou Chen Shuang Wu Ling Shi Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Water Bay Hong Kong SAR Department Of Electronic And Electrical Engineering Shenzhen Key Laboratory of Robotics Perception and Intelligence Southern University of Science and Technology Shenzhen China Noah's Ark Lab Huawei

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

Direct shooting is an efficient method to solve numerical optimal control. It utilizes the Runge-Kutta scheme to discretize a continuous-time optimal control problem making the problem solvable by nonlinear programming solvers. However, conventional direct shooting raises a contradictory dynamics issue when using an augmented state to handle high-order systems. This paper fills the research gap by considering the direct shooting method for high-order systems. We derive the modified Euler and Runge-Kutta-4 methods to transcribe the system dynamics constraint directly. Additionally, we provide the global error upper bounds of our proposed methods. A set of benchmark optimal control problems shows that our methods provide more accurate solutions than existing approaches.

关键词： Upper bound Accuracy System dynamics Optimal control Europe Programming Benchmark testing

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Reinforcement Learning-Based Optimal Multiple Waypoint Navigation

Reinforcement Learning-Based Optimal Multiple Waypoint Navig...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Christos Vlachos Panagiotis Rousseas Charalampos P. Bechlioulis Kostas J. Kyriakopoulos Department of Electrical and Computer Engineering University of Patras Greece School of Mechanical Engineering Control Systems Laboratory National Technical University of Athens Greece Center of AI & Robotics (CAIR) New York University Abu Dhabi

In this paper, a novel method based on Artificial Potential Field (APF) theory is presented, for optimal motion planning in fully-known, static workspaces, for multiple final goal configurations. Optimization is achieved through a Reinforcement Learning (RL) framework. More specifically, the parameters of the underlying potential field are adjusted through a policy gradient algorithm in order to minimize a cost function. The main novelty of the proposed scheme lies in the method that provides optimal policies for multiple final positions, in contrast to most existing methodologies that consider a single final configuration. An assessment of the optimality of our results is conducted by comparing our novel motion planning scheme against a RRT* method.

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Learning a Better Control Barrier Function

arXiv

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arXiv 2022年

作者： Dai, Bolun Krishnamurthy, Prashanth Khorrami, Farshad Control/Robotics Research Laboratory Electrical & Computer Engineering Department Tandon School of Engineering New York University BrooklynNY11201 United States

Control barrier functions (CBFs) are widely used in safety-critical controllers. However, constructing a valid CBF is challenging, especially under nonlinear or non-convex constraints and for high relative degree systems. Meanwhile, finding a conservative CBF that only recovers a portion of the true safe set is usually possible. In this work, starting from a "conservative" handcrafted CBF (HCBF), we develop a method to find a CBF that recovers a reasonably larger portion of the safe set. Since the learned CBF controller is not guaranteed to be safe during training iterations, we use a model predictive controller (MPC) to ensure safety during training. Using the collected trajectory data containing safe and unsafe interactions, we train a neural network to estimate the difference between the HCBF and a CBF that recovers a closer solution to the true safe set. With our proposed approach, we can generate safe controllers that are less conservative and computationally more efficient. We validate our approach on two systems: a second-order integrator and a ball-on-beam. © 2022, CC BY.

关键词： Controllers

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GEM: Context-Aware Gaze EstiMation with Visual Search Behavior Matching for Chest Radiograph

arXiv

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arXiv 2024年

作者： Liu, Shaonan Chen, Wenting Liu, Jie Luo, Xiaoling Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University China Department of Electrical Engineering City University of Hong Kong Hong Kong AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Gaze estimation is pivotal in human scene comprehension tasks, particularly in medical diagnostic analysis. Eye-tracking technology facilitates the recording of physicians’ ocular movements during image interpretation, thereby elucidating their visual attention patterns and information-processing strategies. In this paper, we initially define the context-aware gaze estimation problem in medical radiology report settings. To understand the attention allocation and cognitive behavior of radiologists during the medical image interpretation process, we propose a context-aware Gaze EstiMation (GEM) network that utilizes eye gaze data collected from radiologists to simulate their visual search behavior patterns throughout the image interpretation process. It consists of a context-awareness module, visual behavior graph construction, and visual behavior matching. Within the context-awareness module, we achieve intricate multimodal registration by establishing connections between medical reports and images. Subsequently, for a more accurate simulation of genuine visual search behavior patterns, we introduce a visual behavior graph structure, capturing such behavior through high-order relationships (edges) between gaze points (nodes). To maintain the authenticity of visual behavior, we devise a visual behavior-matching approach, adjusting the high-order relationships between them by matching the graph constructed from real and estimated gaze points. Extensive experiments on four publicly available datasets demonstrate the superiority of GEM over existing methods and its strong generalizability, which also provides a new direction for the effective utilization of diverse modalities in medical image interpretation and enhances the interpretability of models in the field of medical imaging. https://***/Tiger-SN/GEM. Copyright © 2024, The Authors. All rights reserved.

关键词： Eye movements

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