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nnUNet for Brain Tumor Segmentation in Sub-Saharan Africa Patient Population 1

Challenge on Brain Tumor Segmentation, BraTS 2023, International Challenge on Cross-Modality Domain Adaptation for Medical Image Segmentation, CrossMoDA 2023, held in conjunction with the Medical Image Computing for computer Assisted Intervention Conference, MICCAI 2023

作者： Abramova, Valeriia Lal-Trehan Estrada, Uma M. Yalçın, Cansu Hamadache, Rachika E. Clèrigues, Albert Tovar Sáez, Francisco Aarón Guirao, Marc Salvi, Joaquim Oliver, Arnau Lladó, Xavier Computer Vision and Robotics Group University of Girona Girona Spain

ISBN: (数字)9783031761638

ISBN: (纸本)9783031761621

Glioma is one of the most dangerous and aggressive brain tumor types. These tumors are difficult to diagnose, and they require careful and precise identification and delineation of different tumor subtypes. Currently, magnetic resonance imaging (MRI) is the gold standard for glioma detection. However, not all the territories are equipped with up-to-date medical devices and highly skilled professionals, and it is important to develop glioma segmentation methods for the images acquired in such low-resource settings. Therefore, BraTS-Africa challenge was announced in 2023 to provide researchers the opportunity to develop segmentation algorithms using brain MRI glioma cases from Sub-Saharan Africa population. In this paper, we present our submission to this challenge. We based our approach on the well-known nnUNet model, which won original BraTS 2020 and 2021 challenges. Within our work, we studied the impact of using pretrained and fine-tuned models, different image input modalities, ensemble of different models, and the application of specific region based strategies. Obtained results on the unseen testing data showed promising results, having good Dice values for all 3 classes and a small HD95 distance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Magnetic resonance imaging

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Real-Time Multi-object Tracking Using YOLOv8 and SORT on a SoC FPGA 21st

Real-Time Multi-object Tracking Using YOLOv8 and SORT on a...

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21st International Symposium on Applied Reconfigurable Computing, ARC 2025

作者： Danilowicz, Michal Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

ISBN: (纸本)9783031879944

Multi-object tracking (MOT) is one of the most important problems in computer vision and a key component of any vision-based perception system used in advanced autonomous mobile robotics. Therefore, its implementation on low-power and real-time embedded platforms is highly desirable. Modern MOT algorithms should be able to track objects of a given class (e.g. people or vehicles). In addition, the number of objects to be tracked is not known in advance, and they may appear and disappear at any time, as well as be obscured. For these reasons, the most popular and successful approaches have recently been based on the tracking paradigm. Therefore, the presence of a high quality object detector is essential, which in practice accounts for the vast majority of the computational and memory complexity of the whole MOT system. In this paper, we propose an FPGA (Field-Programmable Gate Array) implementation of an embedded MOT system based on a quantized YOLOv8 detector and the SORT (Simple Online Realtime Tracker) tracker. We use a modified version of the FINN framework to utilize external memory for model parameters and to support operations necessary required by YOLOv8. We discuss the evaluation of detection and tracking performance using the COCO and MOT15 datasets, where we achieve 0.21 mAP and 38.9 MOTA respectively. As the computational platform, we use an MPSoC system (Zynq UltraScale+ device from AMD/Xilinx) where the detector is deployed in reprogrammable logic and the tracking algorithm is implemented in the processor system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： System-on-chip

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Energy Efficient Hardware Acceleration of Neural Networks with Power-of-Two Quantisation

Energy Efficient Hardware Acceleration of Neural Networks wi...

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International Conference on computer vision and Graphics, ICCVG 2022

作者： Przewlocka-Rus, Dominika Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

ISBN: (纸本)9783031220241

Deep neural networks virtually dominate the domain of most modern vision systems, providing high performance at a cost of increased computational complexity. Since for those systems it is often required to operate both in real-time and with minimal energy consumption (e.g., for wearable devices or autonomous vehicles, edge Internet of Things (IoT), sensor networks), various network optimisation techniques are used, e.g., quantisation, pruning, or dedicated lightweight architectures. Due to the logarithmic distribution of weights in neural network layers, a method providing high performance with significant reduction in computational precision (for 4-bit weights and less) is the Power-of-Two (PoT) quantisation (and therefore also with a logarithmic distribution). This method introduces additional possibilities of replacing the typical for neural networks Multiply and ACcumulate (MAC—performing, e.g., convolution operations) units, with more energy-efficient Bitshift and ACcumulate (BAC). In this paper, we show that a hardware neural network accelerator with PoT weights implemented on the Zynq UltraScale + MPSoC ZCU104 SoC FPGA can be at least 1.4x more energy efficient than the uniform quantisation version. To further reduce the actual power requirement by omitting part of the computation for zero weights, we also propose a new pruning method adapted to logarithmic quantisation. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： System-on-chip

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Traffic Sign Classification Using Deep and Quantum Neural Networks

Traffic Sign Classification Using Deep and Quantum Neural Ne...

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International Conference on computer vision and Graphics, ICCVG 2022

作者： Kuros, Sylwia Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

ISBN: (纸本)9783031220241

Quantum Neural Networks (QNNs) are an emerging technology that can be used in many applications including computer vision. In this paper, we presented a traffic sign classification system implemented using a hybrid quantum-classical convolutional neural network. Experiments on the German Traffic Sign Recognition Benchmark dataset indicate that currently QNN do not outperform classical DCNN (Deep Convolutuional Neural Networks), yet still provide an accuracy of over 90% and are a definitely promising solution for advanced computer vision. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Traffic signs

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Eye Direction Detection as Wheelchair Navigation Using Faster R-Cnn 23

Eye Direction Detection as Wheelchair Navigation Using Faste...

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8th International Conference on Sustainable Information Engineering and Technology, SIET 2023

作者： Irsal, Riyandi Banovbi Putera Laksono, Blessius Sheldo Putra Utaminingrum, Fitri Computer Vision Research Group Universitas Brawijaya Indonesia Robotics and Embedded System Research Group Universitas Brawijaya East Java Indonesia

ISBN: (纸本)9798400708503

In this life, many people with disabilities lose the opportunity to live better than others. especially people with physical disabilities who may not have mobility like us. Physical disability is caused by several things, some from birth or disease and also an accident. People with physical disabilities who have difficulty walking require a wheelchair to be able to move. However, hands and arms must be used to operate the wheelchair. We found a way to solve the problem. we plan to move the wheelchair with eyeball movement as navigation. this solution does not only solve the problem of physical disability. we can also reduce contact to often hold the wheelchair. To detect eyeball movement, we use the Faster R-CNN method. This method if we compare it with other methods has competitive accuracy. From the results we have, we get a mAP value of more than 0.853 from each class. © 2023 ACM.

关键词： Wheelchairs

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PointPillars Backbone Type Selection for Fast and Accurate LiDAR Object Detection

PointPillars Backbone Type Selection for Fast and Accurate L...

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International Conference on computer vision and Graphics, ICCVG 2022

作者： Lis, Konrad Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Al. Mickiewicza 30 Krakow30-059 Poland

ISBN: (纸本)9783031220241

3D object detection from LiDAR sensor data is an important topic in the context of autonomous cars and drones. In this paper, we present the results of experiments on the impact of backbone selection of a deep convolutional neural network on detection accuracy and computation speed. We chose the PointPillars network, which is characterised by a simple architecture, high speed, and modularity that allows for easy expansion. During the experiments, we paid particular attention to the change in detection efficiency (measured by the mAP metric) and the total number of multiply-addition operations needed to process one point cloud. We tested 10 different convolutional neural network architectures that are widely used in image-based detection problems. For a backbone like MobilenetV1, we obtained an almost 4x speedup at the cost of a 1.13% decrease in mAP. On the other hand, for CSPDarknet we got an acceleration of more than 1.5x at an increase in mAP of 0.33%. We have thus demonstrated that it is possible to significantly speed up a 3D object detector in LiDAR point clouds with a small decrease in detection efficiency. This result can be used when PointPillars or similar algorithms are implemented in embedded systems, including SoC FPGAs. The code is available at https://***/vision-agh/pointpillars-backbone. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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A Semiautomatic Image Processing-Based Method for Binary Segmentation of Lungs in Computed Tomography Images

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SN computer Science 2024年第6期5卷 689页

作者： Ramos, Leo Pineda, Israel School of Mathematical and Computational Sciences Yachay Tech University Deep Learning for Autonomous Driving Robotics and Computer Vision (DeepARC) Research Group Yachay Tech University Kauel Inc. Computer Vision Center Universitat Autònoma de Barcelona College of Sciences and Engineering Universidad San Francisco de Quito

Precise biomedical image segmentation is pivotal in medical diagnosis and treatment. Among various methodologies, image processing-based techniques are useful due to their swift and consistent results, free from the dependency on extensive training data required by deep learning approaches. This study introduces a tri-phase, semi-automated pipeline for lung computed tomography image segmentation, beginning with a preprocessing phase that includes contrast enhancement, noise reduction, and edge detection to improve image quality. This is followed by the application of the region growing method for segmentation and is completed with morphological closing to refine the final output. Comparative tests reveal that our technique surpasses prevalent methods like split and merge, Otsu’s technique, and basic region growing in terms of the dice coefficient (DC) and intersection over union (IoU). Specifically, our approach achieved an average DC of 0.9633, contrasting with 0.4668, 0.005, and 0.4067 recorded by Otsu’s, split and merge, and basic region growing techniques respectively. Similarly, the average IoU for our method was 0.9341, in comparison to 0.3105, 0.0660, and 0.2605 for the aforementioned methods. Additionally, comparative analysis with contemporary segmentation models demonstrates that our approach is competitive, even without relying on large training datasets. These results highlight our method’s efficiency and practicality, distinguishing it as a compelling option in medical scenarios where access to extensive training data is limited. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image analysis Image processing Image segmentation Medical image Pattern recognition Region growing

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Real-Time Multi-Object Tracking using YOLOv8 and SORT on a SoC FPGA

arXiv

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arXiv 2025年

作者： Danilowicz, Michal Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Krakow Poland

关键词： System-on-chip

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Going Smaller: Attention-based models for automated melanoma diagnosis

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computers in Biology and Medicine 2025年 185卷 109492-109492页

作者： Nazari, Sana Garcia, Rafael Computer Vision and Robotics Group University of Girona Plaça de Sant Domènec 3 Girona17004 Spain

Computational approaches offer a valuable tool to aid with the early diagnosis of melanoma by increasing both the speed and accuracy of doctors’ decisions. The latest and best-performing approaches often rely on large ensemble models, with the number of trained parameters exceeding 600 million. However, this large parameter count presents considerable challenges in terms of computational demands and practical application. Addressing this gap, our work introduces a suite of attention-based convolutional neural network (CNN) architectures tailored to the nuanced classification of melanoma. These innovative models, founded on the EfficientNet-B3 backbone, are characterized by their significantly reduced size. This study highlights the feasibility of deploying powerful, yet compact, diagnostic models in practical settings, such as smartphone-based dermoscopy, and in doing so revolutionizing point-of-care diagnostics and extending the reach of advanced medical technologies to remote and under-resourced areas. It presents a comparative analysis of these novel models with the top three prize winners of the International Skin Imaging Collaboration (ISIC) 2020 challenge using two independent test sets. The results for our architectures outperformed the second and third-placed winners and achieved comparable results to the first-placed winner. These models demonstrated a delicate balance between efficiency and accuracy, holding their ground against larger models in performance metrics while operating on up to 98% less number of parameters and showcasing their potential for real-time application in resource-limited environments. © 2024 The Authors

关键词： Dermatology

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An End-to-End Deep Learning Framework for Predicting Hematoma Expansion in Hemorrhagic Stroke Patients from CT Images

SSRN

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SSRN 2024年

作者： Abramova, Valeriia Oliver, Arnau Salvi, Joaquim Terceño, Mikel Silva, Yolanda Lladó, Xavier Computer Vision and Robotics Group University of Girona Catalonia Spain Catalonia Girona Spain

Background and Objectives: Hematoma expansion (HE) occurs in 20% of patients with hemorrhagic stroke within 24 hours of onset and it is associated with a poorer patient outcome. From a clinical point of view, predicting the HE from the initial patient computed tomography (CT) image would be very useful, not only to be able to act faster but to improve therapeutic decisions. Recently, some works have started to study the prediction of the HE as a classification problem to point out if the hematoma will grow or not. However, there are not many works aiming to predict the final extent and appearance of the ***: In this work, we propose an end-to-end deep learning framework for predicting the final hematoma expansion and its corresponding lesion mask. We also explore the problem of having limited data and propose to augment the available dataset with the generation of synthetic images. The proposed approach is evaluated in different scenarios such as training using only the original CT images, using standard data augmentation techniques and using the image synthesis. We perform our experiments in a leave-one-out manner on an in-house dataset consisting of 27 hemorrhagic stroke patients with HE, where longitudinal CT images together with manual lesion annotations were ***: The obtained results showed a promising HE prediction, improving the performance when incorporating the use of synthetic images into the model. Our best model obtained a mean Dice score of the HE growth area of 0.506 and an average prediction error of hematoma volume of -3.44 *** The proposed approach showed a great potential for robust prediction of HE, highlighting also the importance of further investigations in this research line that could improve the management of hemorrhagic stroke patients in clinical practice. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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