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Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions

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arXiv 2022年

作者： Rauniyar, Ashish Haileselassie, Desta Jha, Debesh Håkegård, Jan Erik Bagci, Ulas Rawat, Danda B. Vlassov, Vladimir SINTEF Digital Trondheim7034 Norway Department of Electrical Engineering and Computer Science Howard University WashingtonDC United States Machine & Hybrid Intelligence Lab Department of Radiology Northwestern University United States Department of Computer Science School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

With the advent of the Internet of Things (IoT), Artificial intelligence (AI), machine Learning (ML), and Deep Learning (DL) algorithms, the landscape of data-driven medical applications has emerged as a promising avenue for designing robust and scalable diagnostic and prognostic models from medical data. This has gained a lot of attention from both academia and industry, leading to significant improvements in healthcare quality. However, the adoption of AI-driven medical applications still faces tough challenges, including meeting security, privacy, and quality of service (QoS) standards. Recent developments in Federated Learning (FL) have made it possible to train complex machine-learned models in a distributed manner and has become an active research domain, particularly processing the medical data at the edge of the network in a decentralized way to preserve privacy and address security concerns. To this end, in this paper, we explore the present and future of FL technology in medical applications where data sharing is a significant challenge. We delve into the current research trends and their outcomes, unravelling the complexities of designing reliable and scalable FL models. Our paper outlines the fundamental statistical issues in FL, tackles device-related problems, addresses security challenges, and navigates the complexity of privacy concerns, all while highlighting its transformative potential in the medical field. Our study primarily focuses on medical applications of FL, particularly in the context of global cancer diagnosis. We highlight the potential of FL to enable computer-aided diagnosis tools that address this challenge with greater effectiveness than traditional data-driven methods. Recent literature has shown that FL models are robust and generalize well to new data, which is essential for medical applications. We hope that this comprehensive review will serve as a checkpoint for the field, summarizing the current state-of-the-art and identifyin

关键词： Medical applications

Influence of Manual Inter-Observer Variability for the Performance of Deep Learning Models in Semantic Segmentation

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Influence of Manual Inter-Observer Variability for the Perfo...

International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)

作者： Catalin Stoean Nebojsa Bacanin Ruxandra Stoean Leonard Ionescu Alina-Maria Gărău Cristina-Camelia Ghiţescu Department of Computer Science University of Craiova Artificial Intelligence and Machine Learning Team Romanian Institute of Science and Technology Romania Faculty of Informatics and Computing Singidunum University Belgrade Serbia Restoration and Conservation Lab Oltenia Museum History and Archaeology Section Craiova Romania

Deep learning models for semantic segmentation have expanded their application span from general objects to structures of interest in real-world tasks. The current paper discusses one such practical challenge, i.e. the delineation of corrosion compounds on microscope images taken at the surface of unrestored archaeological artefacts. Apart from the complexity of learning irregular shapes, the influence of the human manual annotations on model training becomes pivotal. The aim of the current paper thus becomes to measure the impact of interobserver variability on the segmentation of four copper corrosion compounds. The results support the need for rigorous guidelines in the manual establishment of the ground truth.

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Mixing Histopathology Prototypes into Robust Slide-Level Representations for Cancer Subtyping

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arXiv 2023年

作者： Butke, Joshua Hashimoto, Noriaki Takeuchi, Ichiro Miyoshi, Hiroaki Ohshima, Koichi Sakuma, Jun Machine Learning and Data Mining Lab University of Tsukuba Japan RIKEN Center for Advanced Intelligence Project Japan Department of Mechanical Systems Engineering Nagoya University Japan Department of Pathology Kurume University Japan Department of Computer Science Tokyo Institute of Technology Japan

Whole-slide image analysis via the means of computational pathology often relies on processing tessellated gigapixel images with only slide-level labels available. Applying multiple instance learning-based methods or transformer models is computationally expensive as, for each image, all instances have to be processed simultaneously. The MLP-Mixer is an under-explored alternative model to common vision transformers, especially for large-scale datasets. Due to the lack of a self-attention mechanism, they have linear computational complexity to the number of input patches but achieve comparable performance on natural image datasets. We propose a combination of feature embedding and clustering to preprocess the full whole-slide image into a reduced prototype representation which can then serve as input to a suitable MLP-Mixer architecture. Our experiments on two public benchmarks and one inhouse malignant lymphoma dataset show comparable performance to current state-of-the-art methods, while achieving lower training costs in terms of computational time and memory load. Code is publicly available at https://***/butkej/ProtoMixer. Copyright © 2023, The Authors. All rights reserved.

关键词： Mixers (machinery)

BiLSTM-Autoencoder Architecture for Stance Prediction

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BiLSTM-Autoencoder Architecture for Stance Prediction

2020 International Conference on Data science and Engineering, ICDSE 2020

作者： Padnekar, S Meena Kumar, G Santhosh Deepak, P. Cochin University of Science and Technology Artificial Intelligence and Computer Vision Lab Department of Computer Science Kochi India Queen's University School of Electronics Electrical Engineering and Computer Science Belfast United Kingdom

ISBN: (纸本)9781728189192

The recent surge in the abundance of fake news appearing on social media and news websites poses a potential threat to high-quality journalism. Misinformation hurts people, society, science, and democracy. This reason has led many researchers to develop techniques to identify fake news. In this paper, we discuss a stance prediction technique using the Deep Learning approach, which can be used as a factor to determine the authenticity of news articles. The Fake News Stance Prediction is the process of automatically classifying the stance of a news article towards a target into one of the following classes: Agree, Disagree, Discuss, Unrelated. The stance prediction task's input is the news articles containing a pair: a headline as the target and a body as a claim. This paper proposes a deep learning architecture using Bi-directional Long Short Term Memory and Autoencoder for stance prediction. We illustrate, through empirical studies, that the method is reasonably accurate at predicting stance, achieving a classification accuracy as high as 94%. The proposed stance detection method would be useful for assessing the credibility of news articles. © 2020 IEEE.

关键词： Forecasting

Cross-Utterance Conditioned VAE for Speech Generation

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arXiv 2023年

作者： Li, Yang Yu, Cheng Sun, Guangzhi Zu, Weiqin Tian, Zheng Wen, Ying Pan, Wei Zhang, Chao Wang, Jun Yang, Yang Sun, Fanglei Department of Computer Science The University of Manchester Manchester United Kingdom The School of Creativity and Art ShanghaiTech University Shanghai China The Machine Intelligence Lab University of Cambridge Cambridge United Kingdom Shanghai Jiao Tong University Shanghai China The Department of Electronic Engineering Tsinghua University Beijing China Department of Speech Hearing and Phonetic Sciences UCL London United Kingdom The Department of Computer Science University College London London United Kingdom Guangzhou511453 China The Department of Computer Science and Engineering University of Shanghai for Science and Technology China

Speech synthesis systems powered by neural networks hold promise for multimedia production, but frequently face issues with producing expressive speech and seamless editing. In response, we present the Cross-Utterance Conditioned Variational Autoencoder speech synthesis (CUC-VAE S2) framework to enhance prosody and ensure natural speech generation. This framework leverages the powerful representational capabilities of pre-trained language models and the re-expression abilities of variational autoencoders (VAEs). The core component of the CUC-VAE S2 framework is the cross-utterance CVAE, which extracts acoustic, speaker, and textual features from surrounding sentences to generate context-sensitive prosodic features, more accurately emulating human prosody generation. We further propose two practical algorithms tailored for distinct speech synthesis applications: CUC-VAE TTS for text-to-speech and CUC-VAE SE for speech editing. The CUC-VAE TTS is a direct application of the framework, designed to generate audio with contextual prosody derived from surrounding texts. On the other hand, the CUC-VAE SE algorithm leverages real mel spectrogram sampling conditioned on contextual information, producing audio that closely mirrors real sound and thereby facilitating flexible speech editing based on text such as deletion, insertion, and replacement. Experimental results on the LibriTTS datasets demonstrate that our proposed models significantly enhance speech synthesis and editing, producing more natural and expressive speech. © 2023, CC BY.

关键词： Spectrographs

AffectiveNet: Affective-motion feature learning for micro expression recognition

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arXiv 2021年

作者： Verma, Monu Vipparthi, Santosh Kumar Singh, Girdhari Vision Intelligence Lab Department of Computer Science and Engineering Malaviya National Institute of Technology Jaipur India

Micro-expressions are hard to spot due to fleeting and involuntary moments of facial muscles. Interpretation of micro emotions from video clips is a challenging task. In this paper we propose an affective-motion imaging that cumulates rapid and short-lived variational information of micro expressions into a single response. Moreover, we have proposed an AffectiveNet: affective-motion feature learning network that can perceive subtle changes and learns the most discriminative dynamic features to describe the emotion classes. The AffectiveNet holds two blocks: MICRoFeat and MFL block. MICRoFeat block conserves the scale-invariant features, which allows network to capture both coarse and tiny edge variations. While MFL block learns micro-level dynamic variations from two different intermediate convolutional layers. Effectiveness of the proposed network is tested over four datasets by using two experimental setups: person independent (PI) and cross dataset (CD) validation. The experimental results of the proposed network outperforms the state-of-the-art approaches with significant margin for MER approaches. © 2021, CC BY.

关键词： machine learning

A Human-machine Joint Learning Framework to Boost Endogenous BCI Training

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arXiv 2023年

作者： Wang, Hanwen Qi, Yu Yao, Lin Wang, Yueming Farina, Dario Pan, Gang The College of Computer Science and Technology Zhejiang University Hangzhou China The Affiliated Mental Health Center Hangzhou Seventh People's Hospital The MOE Frontier Science Center for Brain Science and Brain-machine Integration Zhejiang University School of Medicine The State Key Lab of Brain-Machine Intelligence Hangzhou China The Department of Neurobiology Affiliated Mental Health Center Hangzhou Seventh People's Hospital Zhejiang University School of Medicine MOE Frontiers Science Center for Brain and Brain-Machine Integration Zhejiang University Department of Biomedical Engineering Zhejiang University College of Computer Science and Technology Hangzhou China The Qiushi Academy for Advanced Studies Zhejiang University Hangzhou China The Department of Bioengineering Imperial College London London United Kingdom The State Key Lab of Brain-Machine Intelligence The College of Computer Science and Technology Zhejiang University Hangzhou China The First Affiliated Hospital Zhejiang University Hangzhou China

Brain-computer interfaces (BCIs) provide a direct pathway from the brain to external devices and have demonstrated great potential for assistive and rehabilitation technologies. Endogenous BCIs based on electroencephalogram (EEG) signals, such as motor imagery (MI) BCIs, can provide some level of control. However, mastering spontaneous BCI control requires the users to generate discriminative and stable brain signal patterns by imagery, which is challenging and is usually achieved over a very long training time (weeks/months). Here, we propose a human-machine joint learning framework to boost the learning process in endogenous BCIs, by guiding the user to generate brain signals towards an optimal distribution estimated by the decoder, given the historical brain signals of the user. To this end, we firstly model the human-machine joint learning process in a uniform formulation. Then a human-machine joint learning framework is proposed: 1) for the human side, we model the learning process in a sequential trial-and-error scenario and propose a novel "copy/new" feedback paradigm to help shape the signal generation of the subject toward the optimal distribution;2) for the machine side, we propose a novel adaptive learning algorithm to learn an optimal signal distribution along with the subject's learning process. Specifically, the decoder reweighs the brain signals generated by the subject to focus more on "good" samples to cope with the learning process of the subject. Online and psuedo-online BCI experiments with 18 healthy subjects demonstrated the advantages of the proposed joint learning process over co-adaptive approaches in both learning efficiency and effectiveness. © 2023, CC BY.

关键词： Electroencephalography

HCA-NET: Hierarchical Context Attention Network for Intervertebral Disc Semantic labeling

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HCA-NET: Hierarchical Context Attention Network for Interver...

IEEE International Symposium on Biomedical Imaging

作者： Afshin Bozorgpour Bobby Azad Reza Azad Yury Velichko Ulas Bagci Dorit Merhof Faculty of Informatics and Data Science University of Regensburg Germany Electrical Engineering and Computer Science Department South Dakota State University USA Faculty of Electrical Engineering and Information Technology RWTH Aachen University Germany Machine and Hybrid Intelligence Lab Northwestern University Chicago IL USA Fraunhofer Institute for Digital Medicine MEVIS Germany

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Accurate and automated segmentation of intervertebral discs (IVDs) in medical images is crucial for assessing spine-related disorders, such as osteoporosis, vertebral fractures, or IVD herniation. We present HCA-Net, a novel contextual attention network architecture for semantic labeling of IVDs, with a special focus on exploiting prior geometric information. Our approach excels at processing features across different scales and effectively consolidating them to capture the intricate spatial relationships within the spinal cord. To achieve this, HCA-Net models IVD labeling as a pose estimation problem, aiming to minimize the discrepancy between each predicted IVD location and its corresponding actual joint location. In addition, we introduce a skeletal loss term to reinforce the model’s geometric dependence on the spine. This loss function is designed to constrain the model’s predictions to a range that matches the general structure of the human vertebral skeleton. As a result, the network learns to reduce the occurrence of false predictions and adaptively improves the accuracy of IVD location estimation. Through extensive experimental evaluation on multi-center spine datasets, our approach consistently outperforms previous state-of-the-art methods on both MRI T1w and T2w modalities. The code-base is accessible to the public on GitHub.

关键词： Adaptation models Accuracy Spinal cord Semantics Pose estimation Predictive models Skeleton

A CONCEPTUAL ALGORITHM FOR APPLYING ETHICAL PRINCIPLES OF AI TO MEDICAL PRACTICE

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arXiv 2023年

作者： Jha, Debesh Durak, Gorkem Sharma, Vanshali Keles, Elif Cicek, Vedat Zhang, Zheyuan Srivastava, Abhishek Rauniyar, Ashish Hagos, Desta Haileselassie Tomar, Nikhil Kumar F.H., Miller A., Topcu A., Yazidi J.E., Håkegård U., Bagci Machine & Hybrid Intelligence Lab Department of Radiology Northwestern University United States Sustainable Communication Technologies SINTEF Digital Norway Department of Electrical Engineering and Computer Science Howard University United States Department of General Surgery Tokat State Hospital Tokat Turkey Lab Oslo Metropolitan University Norway

Artificial intelligence (AI) is poised to transform healthcare delivery through revolutionary advances in clinical decision support and diagnostic capabilities. While human expertise remains foundational to medical practice, AI-powered tools are increasingly matching or exceeding specialist-level performance across multiple domains, paving the way for a new era of democratized healthcare access. These systems promise to reduce disparities in care delivery across demographic, racial, and socioeconomic boundaries by providing high-quality diagnostic support at *** a result, advanced healthcare services can be affordable to all populations, irrespective of demographics, race, or socioeconomic background. The democratization of such AI tools can reduce the cost of care, optimize resource allocation, and improve the quality of care. In contrast to humans, AI can potentially uncover complex relationships in the data from a large set of inputs and lead to new evidence-based knowledge in medicine. However, integrating AI into healthcare raises several ethical and philosophical concerns, such as bias, transparency, autonomy, responsibility, and accountability. In this study, we examine recent advances in AI-enabled medical image analysis, current regulatory frameworks, and emerging best practices for clinical integration. We analyze both technical and ethical challenges inherent in deploying AI systems across healthcare institutions, with particular attention to data privacy, algorithmic fairness, and system transparency. Furthermore, we propose practical solutions to address key challenges, including data scarcity, racial bias in training datasets, limited model interpretability, and systematic algorithmic biases. Finally, we outline a conceptual algorithm for responsible AI implementations and identify promising future research and development directions. © 2023, CC BY.

关键词： Resource allocation