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FOUNDATIONAL MODELS IN MEDICAL IMAGING: A COMPREHENSIVE SURVEY AND FUTURE VISION

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arXiv 2023年

作者： Azad, Bobby Azad, Reza Eskandari, Sania Bozorgpour, Afshin Kazerouni, Amirhossein Rekik, Islem Merhof, Dorit Electrical Engineering and Computer Science Department South Dakota State University Brookings United States Faculty of Electrical Engineering and Information Technology RWTH Aachen University Aachen Germany Department of Electrical Engineering University of Kentucky Lexington United States Faculty of Informatics and Data Science University of Regensburg Regensburg Germany BASIRA Lab Imperial-X and Computing Department Imperial College London London United Kingdom School of Electrical Engineering Iran University of Science and Technology Tehran Iran dorit.merhofur.de

Foundation models, large-scale, pre-trained deep-learning models adapted to a wide range of downstream tasks have gained significant interest lately in various deep-learning problems undergoing a paradigm shift with the rise of these models. Trained on large-scale dataset to bridge the gap between different modalities, foundation models facilitate contextual reasoning, generalization, and prompt capabilities at test time. The predictions of these models can be adjusted for new tasks by augmenting the model input with task-specific hints called prompts without requiring extensive labeled data and retraining. Capitalizing on the advances in computer vision, medical imaging has also marked a growing interest in these models. With the aim of assisting researchers in navigating this direction, this survey intends to provide a comprehensive overview of foundation models in the domain of medical imaging. Specifically, we initiate our exploration by providing an exposition of the fundamental concepts forming the basis of foundation models. Subsequently, we offer a methodical taxonomy of foundation models within the medical domain, proposing a classification system primarily structured around training strategies, while also incorporating additional facets such as application domains, imaging modalities, specific organs of interest, and the algorithms integral to these models. Furthermore, we emphasize the practical use case of some selected approaches and then discuss the opportunities, applications, and future directions of these large-scale pre-trained models, for analyzing medical images. In the same vein, we address the prevailing challenges and research pathways associated with foundational models in medical imaging. These encompass the areas of interpretability, data management, computational requirements, and the nuanced issue of contextual comprehension. Finally, we gather the over-viewed studies with their available open-source implementations at our GitHub. We aim

关键词： Medical applications

Acquisition evaluation on outdoor scanning for archaeological artifact digitalization 15

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Acquisition evaluation on outdoor scanning for archaeologica...

15th International Joint Conference on computer Vision, Imaging and computer Graphics Theory and Applications, VISIGRAPP 2020

作者： Kusuma Frisky, Aufaclav Zatu Fajri, Adieyatna Brenner, Simon Sablatnig, Robert Computer Vision Lab Institute of Visual Computing and Human-Centered Technology Faculty of Informatics TU Wien Wien Austria Department of Computer Science and Electronics Universitas Gadjah Mada Yogyakarta Indonesia Department of Archaeology Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (纸本)9789897584022

Archaeological archives are important assets because they provide primary information resources for research, mainly digital archives that not degraded. Instead of directly visiting a site, an archaeologist can examine and manipulate the data without harming the real object. However, choosing an efficient scanning scheme with detailed results is a challenging task. In this work, we present new sculpture models obtained in three different ways and assess it in two comparison approaches: A quantitative and qualitative assessment. The quantitative comparison architecture provides a detailed assessment of three different scanning mechanisms in two stages: point cloud and mesh comparison. This evaluation is purposed to describe the differences between unmodified data. Finally, a qualitative evaluation is performed by an expert and practitioner to explain the difference based on four different produced models to help their needs in the real application. Copyright © 2020 by SCITEPRESS – science and Technology Publications, Lda. All rights reserved.

关键词： Scanning

TransSC: Transformer-based shape completion for grasp evaluation

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arXiv 2021年

作者： Chen, Wenkai Liang, Hongzhuo Chen, Zhaopeng Sun, Fuchun Zhang, Jianwei Department of Informatics Universität Hamburg Germany Agile Robots AG State Key Lab on Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University China

Currently, robotic grasping methods based on sparse partial point clouds have attained a great grasping performance on various objects while they often generate wrong grasping candidates due to the lack of geometric information on the object. In this work, we propose a novel and robust shape completion model (TransSC). This model has a transformer-based encoder to explore more point-wise features and a manifold-based decoder to exploit more object details using a partial point cloud as input. Quantitative experiments verify the effectiveness of the proposed shape completion network and demonstrate it outperforms existing methods. Besides, TransSC is integrated into a grasp evaluation network to generate a set of grasp candidates. The simulation experiment shows that TransSC improves the grasping generation result compared to the existing shape completion baselines. Furthermore, our robotic experiment shows that with TransSC the robot is more successful in grasping objects that are randomly placed on a support surface. Copyright © 2021, The Authors. All rights reserved.

关键词： Robotics

Texture Enhanced Generative Adversarial Network For Stain Normalisation In Histopathology Images

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Texture Enhanced Generative Adversarial Network For Stain No...

IEEE International Symposium on Biomedical Imaging

作者： Cong Cong Sidong Liu Antonio Di Ieva Maurice Pagnucco Shlomo Berkovsky Yang Song School of Computer Science and Engineering University of New South Wales Australia Centre for Health Informatics Macquarie University Australia Computational NeuroSurgery Lab Macquarie University Sydney Australia

ISBN: (纸本)9781665412469;9781665429474

Digitised histopathology image analysis has drawn researchers' attention over recent years. However, stain variation due to several factors can be a significant hurdle for the diagnosis process. Stain normalisation can be used as an effective method to address this issue but most existing methods require careful selection of a reference image. In this work, we propose a texture enhanced pix2pix generative adversarial network (TESGAN), which takes higher contrast hematoxylin components as input and includes a novel loss function to guide the generator to produce higher quality images without the need for reference images. We implement our method as a pre-processing approach for an isocitrate dehydrogenase (IDH) mutation status classification task. Evaluated on The Cancer Genome Atlas (TCGA) glioma cohorts, the proposed model achieves Area Under Curve (AUC) of 0.967, which substantially outperforms the current state-of the-art.

关键词： Image analysis Histopathology Image color analysis Biological system modeling Genomics Generative adversarial networks Generators

Graph Neural Networks in Network Neuroscience

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arXiv 2021年

作者： Bessadok, Alaa Mahjoub, Mohamed Ali Rekik, Islem BASIRA Lab Faculty of Computer and Informatics Istanbul Technical University Istanbul Turkey The School of Science and Engineering Computing University of Dundee United Kingdom LATIS Lab ISITCOM ENISo University of Sousse Sousse Tunisia

Noninvasive medical neuroimaging has yielded many discoveries about the brain connectivity. Several substantial techniques mapping morphological, structural and functional brain connectivities were developed to create a comprehensive road map of neuronal activities in the human brain -namely brain graph. Relying on its non-Euclidean data type, graph neural network (GNN) provides a clever way of learning the deep graph structure and it is rapidly becoming the state-of-the-art leading to enhanced performance in various network neuroscience tasks. Here we review current GNN-based methods, highlighting the ways that they have been used in several applications related to brain graphs such as missing brain graph synthesis and disease classification. We conclude by charting a path toward a better application of GNN models in network neuroscience field for neurological disorder diagnosis and population graph integration. The list of papers cited in our work is available at https://***/basiralab/GNNs-in-Network-Neuroscience. © 2021, CC BY-NC-ND.

关键词： Graph theory

Multi-segment Soft Robot Control via Deep Koopman-based Model Predictive Control

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arXiv 2025年

作者： Lv, Lei Liu, Lei Bao, Lei Sun, Fuchun Dong, Jiahong Zhang, Jianwei Shan, Xuemei Sun, Kai Huang, Hao Luo, Yu Department of Computer Science and Technology Tsinghua University China School of Biomedical Engineering Tsinghua University China Beijing Soft Robot Tech Co. Ltd. China School of Clinical Medicine Tsinghua University China Department of Informatics University of Hamburg Germany School of Mechanical Engineering and Automation Beihang University China Huawei Noah’s Ark Lab Hong Kong Shanghai Research Institute for Intelligent Autonomous Systems Tongji University China

Soft robots, compared to regular rigid robots, as their multiple segments with soft materials bring flexibility and compliance, have the advantages of safe interaction and dexterous operation in the environment. However, due to its characteristics of high dimensional, nonlinearity, time-varying nature, and infinite degree of freedom, it has been challenges in achieving precise and dynamic control such as trajectory tracking and position reaching. To address these challenges, we propose a framework of Deep Koopman-based Model Predictive Control (DK-MPC) for handling multi-segment soft robots. We first employ a deep learning approach with sampling data to approximate the Koopman operator, which therefore linearizes the high-dimensional nonlinear dynamics of the soft robots into a finite-dimensional linear representation. Secondly, this linearized model is utilized within a model predictive control framework to compute optimal control inputs that minimize the tracking error between the desired and actual state trajectories. The real-world experiments on the soft robot "Chordata" demonstrate that DK-MPC could achieve high-precision control, showing the potential of DK-MPC for future applications to soft robots. More visualization results can be found at https://***/DKMPC/. © 2025, CC BY.

关键词： Predictive control systems

A Novel Hybrid Approach to Contraceptive Demand Forecasting: Integrating Point Predictions with Probabilistic Distributions

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arXiv 2025年

作者： Hewage, Harsha Chamara Rostami-Tabar, Bahman Syntetos, Aris Liberatore, Federico Milano, Glenn R. Cardiff University Data Lab for Social Good Group Cardiff Business School CF10 3EU United Kingdom Cardiff University Cardiff Business School CF10 3EU United Kingdom Cardiff University Cardiff School of Computer Science & Informatics CF24 4AG United Kingdom United States Agency for International Development Bureau for Global Health 20523 United States

Accurate demand forecasting is vital for ensuring reliable access to contraceptive products, supporting key processes like procurement, inventory, and distribution. However, forecasting contraceptive demand in developing countries presents challenges, including incomplete data, poor data quality, and the need to account for multiple geographical and product factors. Current methods often rely on simple forecasting techniques, which fail to capture demand uncertainties arising from these factors, warranting expert involvement. Our study aims to improve contraceptive demand forecasting by combining probabilistic forecasting methods with expert knowledge. We developed a hybrid model that combines point forecasts from domain-specific model with probabilistic distributions from statistical and machine learning approaches, enabling human input to fine-tune and enhance the system-generated forecasts. This approach helps address the uncertainties in demand and is particularly useful in resource-limited settings. We evaluate different forecasting methods, including time series, Bayesian, machine learning, and foundational time series methods alongside our new hybrid approach. By comparing these methods, we provide insights into their strengths, weaknesses, and computational requirements. Our research fills a gap in forecasting contraceptive demand and offers a practical framework that combines algorithmic and human expertise. Our proposed model can also be generalized to other humanitarian contexts with similar data *** Codes 90-05 Copyright © 2025, The Authors. All rights reserved.

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DUBLINE: A DEEP UNFOLDING NETWORK FOR B-LINE DETECTION IN LUNG ULTRASOUND IMAGES

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arXiv 2023年

作者： Yang, Tianqi Anantrasirichai, Nantheera Karakuş, Oktay Allinovi, Marco Koydemir, Hatice Ceylan Achim, Alin Visual Information Lab University of Bristol Bristol United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Nephrology Dialysis and Transplantation Careggi University Hospital Florence Italy Department of Biomedical Engineering Texas A&M University United States Center for Remote Health Technologies and Systems Texas A&M Engineering Experiment Station United States

In the context of lung ultrasound, the detection of B-lines, which are indicative of interstitial lung disease and pulmonary edema, plays a pivotal role in clinical diagnosis. Current methods still rely on visual inspection by experts. Vision-based automatic B-line detection methods have been developed, but their performance has yet to improve in terms of both accuracy and computational speed. This paper presents a novel approach to posing B-line detection as an inverse problem via deep unfolding of the Alternating Direction Method of Multipliers (ADMM). It tackles the challenges of data la-belling and model training in lung ultrasound image analysis by harnessing the capabilities of deep neural networks and model-based methods. Our objective is to substantially enhance diagnostic accuracy while ensuring efficient real-time capabilities. The results show that the proposed method runs more than 90 times faster than the traditional model-based method and achieves an F1 score that is 10.6% higher. © 2023, CC BY.

关键词： Inverse problems

Unsupervised Point Cloud Representation Learning with Deep Neural Networks: A Survey

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arXiv 2022年

作者： Xiao, Aoran Huang, Jiaxing Guan, Dayan Zhang, Xiaoqin Lu, Shijian Shao, Ling The School of Computer Science and Engineering Nanyang Technological University Singapore Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province Wenzhou University China UCAS-Terminus AI Lab UCAS United Kingdom

Point cloud data have been widely explored due to its superior accuracy and robustness under various adverse situations. Meanwhile, deep neural networks (DNNs) have achieved very impressive success in various applications such as surveillance and autonomous driving. The convergence of point cloud and DNNs has led to many deep point cloud models, largely trained under the supervision of large-scale and densely-labelled point cloud data. Unsupervised point cloud representation learning, which aims to learn general and useful point cloud representations from unlabelled point cloud data, has recently attracted increasing attention due to the constraint in large-scale point cloud labelling. This paper provides a comprehensive review of unsupervised point cloud representation learning using DNNs. It first describes the motivation, general pipelines as well as terminologies of the recent studies. Relevant background including widely adopted point cloud datasets and DNN architectures is then briefly presented. This is followed by an extensive discussion of existing unsupervised point cloud representation learning methods according to their technical approaches. We also quantitatively benchmark and discuss the reviewed methods over multiple widely adopted point cloud datasets. Finally, we share our humble opinion about several challenges and problems that could be pursued in the future research in unsupervised point cloud representation learning. A project associated with this survey has been built at https://***/xiaoaoran/3d_url_survey. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks