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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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Optimisation of a Siamese Neural Network for Real-Time Energy Efficient Object Tracking 1

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International Conference on computer vision and Graphics, ICCVG 2020

作者： Przewlocka, Dominika Wasala, Mateusz Szolc, Hubert Blachut, Krzysztof Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

ISBN: (数字)9783030590062

ISBN: (纸本)9783030590055

In this paper the research on optimisation of visual object tracking using a Siamese neural network for embedded vision systems is presented. It was assumed that the solution shall operate in real-time, preferably for a high resolution video stream, with the lowest possible energy consumption. To meet these requirements, techniques such as the reduction of computational precision and pruning were considered. Brevitas, a tool dedicated for optimisation and quantisation of neural networks for FPGA implementation, was used. A number of training scenarios were tested with varying levels of optimisations – from integer uniform quantisation with 16 bits to ternary and binary networks. Next, the influence of these optimisations on the tracking performance was evaluated. It was possible to reduce the size of the convolutional filters up to 10 times in relation to the original network. The obtained results indicate that using quantisation can significantly reduce the memory and computational complexity of the proposed network while still enabling precise tracking, thus allow to use it in embedded vision systems. Moreover, quantisation of weights positively affects the network training by decreasing overfitting. © 2020, Springer Nature Switzerland AG.

关键词： Field programmable gate arrays (FPGA)

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A vision Based Hardware-Software Real-Time Control System for the Autonomous Landing of an UAV 1

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International Conference on computer vision and Graphics, ICCVG 2020

作者： Blachut, Krzysztof Szolc, Hubert Wasala, Mateusz Kryjak, Tomasz Gorgon, Marek Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

ISBN: (数字)9783030590062

ISBN: (纸本)9783030590055

In this paper we present a vision based hardware-software control system enabling the autonomous landing of a multirotor unmanned aerial vehicle (UAV). It allows for the detection of a marked landing pad in real-time for a 1280 720 @ 60 fps video stream. In addition, a LiDAR sensor is used to measure the altitude above ground. A heterogeneous Zynq SoC device is used as the computing platform. The solution was tested on a number of sequences and the landing pad was detected with 96% accuracy. This research shows that a reprogrammable heterogeneous computing system is a good solution for UAVs because it enables real-time data stream processing with relatively low energy consumption. © 2020, Springer Nature Switzerland AG.

关键词： System-on-chip

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FeCAM: Exploiting the Heterogeneity of Class Distributions in Exemplar-Free Continual Learning

arXiv

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arXiv 2023年

作者： Goswami, Dipam Liu, Yuyang Twardowski, Bartlomiej van de Weijer, Joost Department of Computer Science Universitat Autònoma de Barcelona Spain Computer Vision Center Barcelona Spain University of Chinese Academy of Sciences China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences China IDEAS-NCBR

Exemplar-free class-incremental learning (CIL) poses several challenges since it prohibits the rehearsal of data from previous tasks and thus suffers from catastrophic forgetting. Recent approaches to incrementally learning the classifier by freezing the feature extractor after the first task have gained much attention. In this paper, we explore prototypical networks for CIL, which generate new class prototypes using the frozen feature extractor and classify the features based on the Euclidean distance to the prototypes. In an analysis of the feature distributions of classes, we show that classification based on Euclidean metrics is successful for jointly trained features. However, when learning from non-stationary data, we observe that the Euclidean metric is suboptimal and that feature distributions are heterogeneous. To address this challenge, we revisit the anisotropic Mahalanobis distance for CIL. In addition, we empirically show that modeling the feature covariance relations is better than previous attempts at sampling features from normal distributions and training a linear classifier. Unlike existing methods, our approach generalizes to both many- and few-shot CIL settings, as well as to domain-incremental settings. Interestingly, without updating the backbone network, our method obtains state-of-the-art results on several standard continual learning benchmarks. Code is available at https://***/dipamgoswami/FeCAM. © 2023, CC BY.

关键词： Normal distribution

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Efficiently Approaching Groups of People in a Socially Acceptable Manner in Environments with Obstacles

Efficiently Approaching Groups of People in a Socially Accep...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Aline F. F. Silva Luciano E. Almeida Douglas G. Macharet Department of Computer Science Computer Vision and Robotics Laboratory (VeRLab) Universidade Federal de Minas Gerais Brazil Laboratório de Inteligência Computacional e Robótica (LICRo) Instituto Federal do Triângulo Mineiro (IFTM) Brazil

Advancements in mobile robotics have allowed humans and robots to interact in different environments and ways. A problem of great interest in Human-Robot Interaction is how to approach individuals, e.g., to gather information, in a socially acceptable manner. We present a new method for planning sequential visits to various groups of people in cluttered environments. The problem is formulated as a Set Orienteering Problem, where each group denotes a cluster with a set of possible approaching points considering different F-formations. We use the concept of a social probabilistic roadmap to determine safe paths between groups. Simulations considering different cases show that methodology produces efficient tours that maximize the number of approached individuals while respecting social norms of distance and a limited budget.

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Rediscovering BCE Loss for Uniform Classification

arXiv

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arXiv 2024年

作者： Li, Qiufu Jia, Xi Zhou, Jiancan Shen, Linlin Duan, Jinming National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Computer Vision Institute Shenzhen University SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China School of Computer Science University of Birmingham United Kingdom Aqara Lumi United Technology Co. Ltd China

This paper introduces the concept of uniform classification, which employs a unified threshold to classify all samples rather than adaptive threshold classifying each individual sample. We also propose the uniform classification accuracy as a metric to measure the model’s performance in uniform classification. Furthermore, begin with a naive loss, we mathematically derive a loss function suitable for the uniform classification, which is the BCE function integrated with a unified bias. We demonstrate the unified threshold could be learned via the bias. The extensive experiments on six classification datasets and three feature extraction models show that, compared to the SoftMax loss, the models trained with the BCE loss not only exhibit higher uniform classification accuracy but also higher sample-wise classification accuracy. In addition, the learned bias from BCE loss is very close to the unified threshold used in the uniform classification. The features extracted by the models trained with BCE loss not only possess uniformity but also demonstrate better intra-class compactness and inter-class distinctiveness, yielding superior performance on open-set tasks such as face recognition. © 2024, CC BY.

关键词： Classification (of information)

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Towards Combating Frequency Simplicity-biased Learning for Domain Generalization

arXiv

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arXiv 2024年

作者： He, Xilin Hu, Jingyu Lin, Qinliang Luo, Cheng Xie, Weicheng Song, Siyang Khan, Muhammad Haris Shen, Linlin Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China Guangdong Provincial Key Laboratory of Intelligent Information Processing China University of Exeter United Kingdom Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates

Domain generalization methods aim to learn transferable knowledge from source domains that can generalize well to unseen target domains. Recent studies show that neural networks frequently suffer from a simplicity-biased learning behavior which leads to over-reliance on specific frequency sets, namely as frequency shortcuts, instead of semantic information, resulting in poor generalization performance. Despite previous data augmentation techniques successfully enhancing generalization performances, they intend to apply more frequency shortcuts, thereby causing hallucinations of generalization improvement. In this paper, we aim to prevent such learning behavior of applying frequency shortcuts from a data-driven perspective. Given the theoretical justification of models’ biased learning behavior on different spatial frequency components, which is based on the dataset frequency properties, we argue that the learning behavior on various frequency components could be manipulated by changing the dataset statistical structure in the Fourier domain. Intuitively, as frequency shortcuts are hidden in the dominant and highly dependent frequencies of dataset structure, dynamically perturbating the over-reliance frequency components could prevent the application of frequency shortcuts. To this end, we propose two effective data augmentation modules designed to collaboratively and adaptively adjust the frequency characteristic of the dataset, aiming to dynamically influence the learning behavior of the model and ultimately serving as a strategy to mitigate shortcut learning. Code is available at AdvFrequency. © 2024, CC BY.

关键词： Contrastive Learning

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Energy-efficient Trajectory Planning with Media Transition for a Hybrid Unmanned Aerial-Underwater Vehicle

Energy-efficient Trajectory Planning with Media Transition f...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Pedro M. Pinheiro Armando Alves Neto Douglas G. Macharet Paulo L. J. Drews NAUTEC Center for Computational Science Universidade Federal do Rio Grande - FURG Rio Grande Brazil Dept. of Electronics Engineering Univ. Federal de Minas Gerais Belo Horizonte Brazil Dep. of Computer Science Computer Vision and Robotics Laboratory (VeRLab) Univ. Federal de Minas Gerais Belo Horizonte Brazil

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Vehicles capable of operating in more than one environment have been developed to solve real problems. Among them, the hybrid unmanned aerial-underwater vehicle (HUAUV) is receiving attention from the robotics community, mainly with a quadrotor-like configuration. However, this vehicle presents high energy consumption because of the larger mass required compared to the only aerial vehicle, limiting its autonomy. This work addresses the trajectory planning problem for a HUAUV. The method is based on Rapidly-exploring Random Trees (RRTs), a highly customizable planning technique. In addition, we propose two new heuristics to increase the energy efficiency of the hybrid vehicle. The first consists of biasing the tree expansion towards the environment with the lowest navigation cost, while the second one assigns estimated costs to nodes in the tree and chooses the least expensive trajectories. These techniques are evaluated in physically realistic simulation experiments performed in 135 scenarios. A comparative analysis of their performances is presented relative to the state of the art. We show that using efficient heuristics can significantly contribute to reducing energy consumption and even increase the average velocity in the missions performed by these vehicles.

关键词： Energy consumption Costs Limiting Trajectory planning Navigation Media Energy efficiency Trajectory Planning Intelligent robots

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Traffic Sign Detection With Event Cameras and DCNN

arXiv

引用

arXiv 2022年

作者： Wzorek, Piotr Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Kraków Poland Department of Digital Systems Silesian University of Technology Gliwice Poland

In recent years, event cameras (DVS – Dynamic vision Sensors) have been used in vision systems as an alternative or supplement to traditional cameras. They are characterised by high dynamic range, high temporal resolution, low latency, and reliable performance in limited lighting conditions – parameters that are particularly important in the context of advanced driver assistance systems (ADAS) and self-driving cars. In this work, we test whether these rather novel sensors can be applied to the popular task of traffic sign detection. To this end, we analyse different representations of the event data: event frame, event frequency, and the exponentially decaying time surface, and apply video frame reconstruction using a deep neural network called FireNet. We use the deep convolutional neural network YOLOv4 as a detector. For particular representations, we obtain a detection accuracy in the range of 86.9-88.9% mAP@0.5. The use of a fusion of the considered representations allows us to obtain a detector with higher accuracy of 89.9% mAP@0.5. In comparison, the detector for the frames reconstructed with FireNet is characterised by an accuracy of 72.67% mAP@0.5. The results obtained illustrate the potential of event cameras in automotive applications, either as standalone sensors or in close cooperation with typical frame-based cameras. © 2022, CC BY.

关键词： Cameras

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Pointsurface: Discriminative Point Cloud Surface Feature Extraction for 3d Face Recognition

SSRN

引用

SSRN 2024年

作者： Yang, Junpeng Li, Qiufu Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen University Guangdong Shenzhen518172 China

3D face recognition performs better than 2D face recognition, in terms of robustness against lighting and digital attacks. Compared to 2D data, the rich geometric information in 3D data could be very useful to improve the performance of 3D face recognition. This paper introduces PointSurFace, a surface feature extraction integrated model tailored for point cloud based 3D face recognition. PointSurFace adopts a specially designed surface feature extraction module, which explores information about the position and normals of the 3D face surface to explicitly extract local discriminative features from 3D point cloud faces. Subsequently, it alternates between set abstraction (SA) layers and Inverted Residual MLP (InvResMLP) modules to effectively extract features from the point cloud data. To better handle the spatial relationships in the point cloud data, PointSurFace integrates coordinate and feature data within each SA layer and InvResMLP module. It also applies channel fusion operations to align these different types of data, thereby more effectively extracts point cloud features. Experimental results in 3D facial recognition and verification across multiple datasets demonstrate that PointSurFace achieves the best 3D face recognition performance to date. For example, on Lock3DFace, it achieves the state-of-the-art recognition and verification accuracy of 90.03% and 80.01%, respectively, surpassing previous methods by 2.02% and 3.19%. Results on cross-quality datasets also affirm PointSurFace’s strong generalization capabilities. Ablation experiments validate the effectiveness of each module within PointSurFace, which suggests that the proposed surface feature extraction module significantly enhances the point cloud based 3D facial recognition performance. © 2024, The Authors. All rights reserved.

关键词： Feature extraction

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