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12th International Conference on Learning Representations, ICLR 2024

作者： Blumberg, Stefano B. Slator, Paddy J. Alexander, Daniel C. Centre for Artificial Intelligence Department of Computer Science University College London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London United Kingdom Cardiff University Brain Research Imaging Centre School of Computer Science Cardiff University United Kingdom

This paper presents a data-driven, task-specific paradigm for experimental design, to shorten acquisition time, reduce costs, and accelerate the deployment of imaging devices. Current approaches in experimental design focus on model-parameter estimation and require specification of a particular model, whereas in imaging, other tasks may drive the design. Furthermore, such approaches often lead to intractable optimization problems in real-world imaging applications. Here we present a new paradigm for experimental design that simultaneously optimizes the design (set of image channels) and trains a machine-learning model to execute a user-specified image-analysis task. The approach obtains data densely-sampled over the measurement space (many image channels) for a small number of acquisitions, then identifies a subset of channels of prespecified size that best supports the task. We propose a method: TADRED for TAsk-DRiven Experimental Design in imaging, to identify the most informative channel-subset whilst simultaneously training a network to execute the task given the subset. Experiments demonstrate the potential of TADRED in diverse imaging applications: several clinically-relevant tasks in magnetic resonance imaging;and remote sensing and physiological applications of hyperspectral imaging. Results show substantial improvement over classical experimental design, two recent application-specific methods within the new paradigm, and state-of-the-art approaches in supervised feature selection. We anticipate further applications of our approach. Code is available: Code Link. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Magnetic resonance imaging

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A Two-Stage Self-Supervised Learning Framework for Automated Whole Heart Segmentation in CT and MRI: Addressing Challenges in Cardiac Imaging 1st

A Two-Stage Self-Supervised Learning Framework for Automated...

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1st MICCAI Challenge Comprehensive Analysis and computing of Real-World medical images, CARE 2024 Held in Conjunction with 27th International Conference on medical image computing and computer-Assisted Intervention, MICCAI 2024

作者： Qayyum, Abdul Mazher, Moona Niederer, Steven A. Faculty of Medicine National Heart and Lung Institute Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom

ISBN: (纸本)9783031870088

Cardiovascular diseases (CVDs) are the leading cause of death worldwide, highlighting the need for precise diagnostic and therapeutic strategies. Whole heart segmentation (WHS) from medical images is vital for understanding cardiac anatomy, assessing disease progression, and guiding treatments. However, WHS poses significant challenges due to the dynamic shape changes of the heart during the cardiac cycle, motion artifacts, low contrast-to-noise ratio, and variability from multi-center data. Additionally, CT and MRI modalities each present unique difficulties: CT provides high spatial resolution but poor soft-tissue contrast, while MRI offers excellent soft-tissue contrast but is prone to artifacts. To address these challenges, we propose a two-stage method. In the first stage, the DINOv2 framework leverages a 3D Vision-LSTM (xLSTM) model for self-supervised learning on the limited, unlabeled WHS++ dataset to overcome the scarcity of annotated data. In the second stage, we fine-tune the model for segmentation by freezing the encoder and optimizing the decoder. Our approach is rigorously evaluated on the WHS++ dataset for both CT and MRI modalities, demonstrating superior performance compared to state-of-the-art deep learning models in accurately segmenting cardiac structures. Our proposed solution achieved an average Dice Coefficient of 0.9414 using CT and 0.8891 using MRI dataset. This work advances the field of cardiac imaging by providing a robust, generalizable solution for automated whole heart segmentation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： computerized tomography

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Disentangled Diffusion Autoencoder for Harmonization of Multi-site Neuroimaging Data

arXiv

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arXiv 2024年

作者： Ijishakin, Ayodeji Aguila, Ana Lawry Levitis, Elizabeth Abdulaal, Ahmed Altmann, Andre Cole, James Centre for Medical Image Computing Department of Computer Science University College London United Kingdom

Combining neuroimaging datasets from multiple sites and scanners can help increase statistical power and thus provide greater insight into subtle neuroanatomical effects. However, site-specific effects pose a challenge by potentially obscuring the biological signal and introducing unwanted variance. Existing harmonization techniques, which use statistical models to remove such effects, have been shown to incompletely remove site effects while also failing to preserve biological variability. More recently, generative models using GANs or autoencoder-based approaches, have been proposed for site adjustment. However, such methods are known for instability during training or blurry image generation. In recent years, diffusion models have become increasingly popular for their ability to generate high-quality synthetic images. In this work, we introduce the disentangled diffusion autoencoder (DDAE), a novel diffusion model designed for controlling specific aspects of an image. We apply the DDAE to the task of harmonizing MR images by generating high-quality site-adjusted images that preserve biological variability. We use data from 7 different sites and demonstrate the DDAE’s superiority in generating high-resolution, harmonized 2D MR images over previous approaches. As far as we are aware, this work marks the first diffusion-based model for site adjustment of neuroimaging data. © 2024, CC BY-NC-SA.

关键词： Magnetic resonance imaging

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Alternative Learning Paradigms for image Quality Transfer

arXiv

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arXiv 2024年

作者： Eldaly, Ahmed Karam Figini, Matteo Alexander, Daniel C. Centre for Medical Image Computing Department of Computer Science University College London United Kingdom Department of Computer Science University of Exeter United Kingdom

image Quality Transfer (IQT) aims to enhance the contrast and resolution of low-quality medical images, e.g. obtained from low-power devices, with rich information learned from higher quality images. In contrast to existing IQT methods in the literature which adopt supervised learning frameworks, in this work, we propose two novel formulations of the IQT problem. The first approach uses an unsupervised learning framework, whereas the second is a combination of both supervised and unsupervised learning. The unsupervised learning approach considers a sparse representation (SRep) and dictionary learning model, which we call IQT-SRep, whereas the combination of supervised and unsupervised learning approach is based on deep dictionary learning (DDL), which we call IQT-DDL. The IQT-SRep approach trains two dictionaries using a sparse representation model using pairs of low- and high-quality volumes. Subsequently, the sparse representation of a low-quality block, in terms of the low-quality dictionary, can be directly used to recover the corresponding high-quality block using the high-quality dictionary. On the other hand, the IQT-DDL approach explicitly learns a high-resolution dictionary to upscale the input volume, while the entire network, including high dictionary generator, is simultaneously optimised to take full advantage of deep learning methods. The two models are evaluated using a low-field magnetic resonance imaging (MRI) application aiming to recover high-quality images akin to those obtained from high-field scanners. Experiments comparing the proposed approaches against state-of-the-art supervised deep learning IQT method (IQT-DL) identify that the two novel formulations of the IQT problem can avoid bias associated with supervised methods when tested using out-of-distribution data that differs from the distribution of the data the model was trained on. This highlights the potential benefit of these novel paradigms for IQT. © 2024, CC BY.

关键词： Self-supervised learning

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Digital Twins and Cyber-Physical Systems:A New Frontier in computer Modeling

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computer Modeling in Engineering & sciences 2025年第4期143卷 51-113页

作者： Vidyalakshmi G S Gopikrishnan Wadii Boulila Anis Koubaa Gautam Srivastava Department of Computer Science and Engineering SRM University APAmaravati522240India School of Computer Science and Engineering VIT-AP UniversityAmaravathi522240India Robotics and Internet-of-Things Laboratory Prince Sultan UniversityRiyadh12435Saudi Arabia Department of Math and Computer Science Brandon UniversityBrandonMB R7A6A9Canada Research Centre for Interneural Computing China Medical UniversityTaichung40402Taiwan

Cyber-Physical Systems(CPS)represent an integration of computational and physical elements,revolutionizing industries by enabling real-time monitoring,control,and optimization.A complementary technology,Digital Twin(DT),acts as a virtual replica of physical assets or processes,facilitating better decision making through simulations and predictive *** and DT underpin the evolution of Industry 4.0 by bridging the physical and digital *** survey explores their synergy,highlighting how DT enriches CPS with dynamic modeling,realtime data integration,and advanced simulation *** layered architecture of DTs within CPS is examined,showcasing the enabling technologies and tools vital for seamless *** study addresses key challenges in CPS modeling,such as concurrency and communication,and underscores the importance of DT in overcoming these *** in various sectors are analyzed,including smart manufacturing,healthcare,and urban planning,emphasizing the transformative potential of CPS-DT *** addition,the review identifies gaps in existing methodologies and proposes future research directions to develop comprehensive,scalable,and secure CPSDT *** synthesizing insights fromthe current literature and presenting a taxonomy of CPS and DT,this survey serves as a foundational reference for academics and *** findings stress the need for unified frameworks that align CPS and DT with emerging technologies,fostering innovation and efficiency in the digital transformation era.

关键词： Cyber physical systems digital twin efficiency Industry 4.0 robustness and intelligence

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Efficient Deep Learning Models for Ultra-widefield Fundus Imaging for Diabetic Retinopathy 1st

Efficient Deep Learning Models for Ultra-widefield Fundus Im...

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1st MICCAI Challenge on Ultra-Widefield Fundus Imaging for Diabetic Retinopathy, UWF4DR 2024, Held in Conjunction with 27th International Conference on medical image computing and computer Assisted Intervention, MICCAI 2024

作者： Qayyum, Abdul Mazher, Moona Niederer, Steven A. National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom

ISBN: (纸本)9783031893872

Diabetic retinopathy (DR) is a leading cause of preventable blindness among working-age adults, with global cases expected to rise from 103 million in 2020 to 161 million by 2045. Early detection and treatment are essential for preventing vision loss. While color fundus photography (CFP) is the gold standard for DR diagnosis, ultra-widefield (UWF) fundus imaging, which provides up to a 200-degree view of the retina, is gaining traction as an alternative. However, classifying UWF images remains labor-intensive, and research on automated analysis is limited. In this study, we trained several deep-learning models for DR detection and assessment using UWF imaging. For Task 1, image Quality Assessment, we utilized the DINOv2 models due to their ability to efficiently manage complex features while maintaining computational efficiency. For Task 2, Identification of Referable DR, we employed MobileNetV3, which stood out for its lightweight architecture and advanced features like Squeeze-and-Excitation modules and H-Swish activation. MobileNetV3 achieved high accuracy with minimal computational requirements, making it well-suited for clinical deployment in resource-limited environments. To further enhance the model performance, we applied test-time augmentation (TTA) during inference, improving robustness and accuracy in challenging cases. This approach offers a powerful and efficient solution for DR classification and detection in UWF fundus images. Our proposed model ranked second in identifying referable DR on the final test dataset. Souce code is available at https://***/RespectKnowledge/UWF4DR_Challenge_2nd_place. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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LEARNING TO DOWNSAMPLE FOR SEGMENTATION OF ULTRA-HIGH RESOLUTION imageS 10

LEARNING TO DOWNSAMPLE FOR SEGMENTATION OF ULTRA-HIGH RESOLU...

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10th International Conference on Learning Representations, ICLR 2022

作者： Jin, Chen Tanno, Ryutaro Mertzanidou, Thomy Panagiotaki, Eleftheria Alexander, Daniel C. Centre for Medical Image Computing Department of Computer Science University College London United Kingdom Healthcare Intelligence Microsoft Research Cambridge United Kingdom

Many computer vision systems require low-cost segmentation algorithms based on deep learning, either because of the enormous size of input images or limited computational budget. Common solutions uniformly downsample the input images to meet memory constraints, assuming all pixels are equally informative. In this work, we demonstrate that this assumption can harm the segmentation performance because the segmentation difficulty varies spatially (see Figure 1 "Uniform"). We combat this problem by introducing a learnable downsampling module, which can be optimised together with the given segmentation model in an end-to-end fashion. We formulate the problem of training such downsampling module as optimisation of sampling density distributions over the input images given their low-resolution views. To defend against degenerate solutions (e.g. over-sampling trivial regions like the backgrounds), we propose a regularisation term that encourages the sampling locations to concentrate around the object boundaries. We find the downsampling module learns to sample more densely at difficult locations, thereby improving the segmentation performance (see Figure 1 "Ours"). Our experiments on benchmarks of high-resolution street view, aerial and medical images demonstrate substantial improvements in terms of efficiency-and-accuracy trade-off compared to both uniform downsampling and two recent advanced downsampling techniques. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

关键词： Antennas

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Interpretable Alzheimer's Disease Classification Via a Contrastive Diffusion Autoencoder

arXiv

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arXiv 2023年

作者： Ijishakin, Ayodeji Abdulaal, Ahmed Hadjivasiliou, Adamos Martin, Sophie Cole, James Centre for Medical Image Computing Department of Computer Science University College London United Kingdom

In visual object classification, humans often justify their choices by comparing objects to prototypical examples within that class. We may therefore increase the interpretability of deep learning models by imbuing them with a similar style of reasoning. In this work, we apply this principle by classifying Alzheimer's Disease based on the similarity of images to training examples within the latent space. We use a contrastive loss combined with a diffusion autoencoder backbone, to produce a semantically meaningful latent space, such that neighbouring latents have similar image-level features. We achieve a classification accuracy comparable to black box approaches on a dataset of 2D MRI images, whilst producing human interpretable model explanations. Therefore, this work stands as a contribution to the pertinent development of accurate and interpretable deep learning within medical imaging. Code available at: Contrast-DiffAE. © 2023, CC BY-NC-ND.

关键词： Neurodegenerative diseases

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Automated Ensemble Multimodal Machine Learning for Healthcare

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IEEE Journal of Biomedical and Health Informatics 2025年第6期29卷 4213-4226页

作者： Imrie, Fergus Denner, Stefan Brunschwig, Lucas S. Maier-Hein, Klaus Van Der Schaar, Mihaela University of Oxford Department of Statistics Oxford United Kingdom Heidelberg University Faculty of Mathematics and Computer Science Germany École Polytechnique Fédérale de Lausanne Switzerland Division of Medical Image Computing Germany Heidelberg University Hospital Pattern Analysis and Learning Group Department of Radiation Oncology Germany Heidelberg Germany University of Cambridge Department of Applied Mathematics and Theoretical Physics United Kingdom

The application of machine learning in medicine and healthcare has led to the creation of numerous diagnostic and prognostic models. However, despite their success, current approaches generally issue predictions using data from a single modality. This stands in stark contrast with clinician decision-making which employs diverse information from multiple sources. While several multimodal machine learning approaches exist, significant challenges in developing multimodal systems remain that are hindering clinical adoption. In this paper, we introduce a multimodal framework, AutoPrognosis-M, that enables the integration of structured clinical (tabular) data and medical imaging using automated machine learning. AutoPrognosis-M incorporates 17 imaging models, including convolutional neural networks and vision transformers, and three distinct multimodal fusion strategies. In an illustrative application using a multimodal skin lesion dataset, we highlight the importance of multimodal machine learning and the power of combining multiple fusion strategies using ensemble learning. We have open-sourced our framework as a tool for the community and hope it will accelerate the uptake of multimodal machine learning in healthcare and spur further innovation. © 2013 IEEE.

关键词： Adversarial machine learning

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IDS-INT:Intrusion detection system using transformer-based transfer learning for imbalanced network traffic

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Digital Communications and Networks 2024年第1期10卷 190-204页

作者： Farhan Ullah Shamsher Ullah Gautam Srivastava Jerry Chun-Wei Lin School of Software Northwestern Polytechnical UniversityXian710072ShaanxiChina Knowledge Unit of Systems and Technology University of Management and TechnologySialkot 51040Pakistan Department of Math and Computer Science Brandon UniversityBrandonMB R7A 6A9Canada Department of Computer Science and Math Lebanese American UniversityBeirut1102Lebanon Western Norway University of Applied Sciences BergenNorway Research Centre for Interneural Computing China Medical UniversityTaichungTaiwanChina

A network intrusion detection system is critical for cyber security against llegitimate *** terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,*** terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal *** is challenging to identify a specific attack due to complex features and data imbalance *** address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced ***,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,***,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic ***,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority ***,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network ***,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep *** experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and *** explainable AI approach is implemented to interpret the proposed method and develop a trustable model.

关键词： Network intrusion detection Transfer learning Features extraction Imbalance data Explainable AI Cybersecurity

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