Quantum Machine Learning Applications to Medical Images: A Survey

At the first annual Conference on Machine Intelligence in Medical Imaging (C-MIMI), held in September 2016, a conference session on medical image data and datasets for machine learning identified multiple issues. The common theme from attendees was that everyone participating in medical image evaluation with machine learning is data starved. There is an urgent need to find better ways to collect, annotate, and reuse medical imaging data. Unique domain issues with medical image datasets require further study, development, and dissemination of best practices and standards, and a coordinated effort among medical imaging domain experts, medical imaging informaticists, government and industry data scientists, and interested commercial, academic, and government entities. High-level attributes of reusable medical image datasets suitable to train, test, validate, verify, and regulate ML products should be better described. NIH and other government agencies should promote and, where applicable, enforce, access to medical image datasets. We should improve communication among medical imaging domain experts, medical imaging informaticists, academic clinical and basic science researchers, government and industry data scientists, and interested commercial entities.

Machine learning improved by quantum computing. Machine learning and quantum physics fix AI and computers. This chapter discusses quantum machine learning theory, methods, and applications. Part 1 thoroughly discusses quantum and classical machine learning. The authors demonstrate how quantum supports vector machines, neural networks, and clustering speed AI. The chapter examines quantum machine learning's merits and downsides. Quantum computers optimize, parallelize, and manage huge data better. Quantum hardware restrictions and error correction reduce noise and decoherence. Explore quantum machine learning in NLP, drug discovery, financial modeling, and image recognition. Many fields could change quantum platform machine learning models with quantum algorithms. The chapter concludes with quantum machine learning directions and challenges. Check trustworthy quantum machine learning frameworks, benchmarks, and hybrid algorithms. Hot: quantum machine learning. This chapter covers fundamentals, research frameworks, and applications.

Quantum Computing is new standard which will contribute computational efficiency on to the many operational methods of classical computing. Quantum computing motivates to use of quantum mechanics such as superposition and entanglement for making new standard of computation which will be far different than classical computer. The quantum computing concept need to understand Qubit which is nothing but Quantum Bit that differs quantum computing from classical computing. Classical bit, which can be either Zero 0 or One 1 in single state at a time moment, a Qubit or Quantum Bit can be Zero 0 and One 1 at same time called as in superposition state. Quantum Computers will use quantum superposition and quantum entanglement are the two basic laws of quantum physics principles. Computational tasks which are non-computable by classical machine can be solved by quantum computer and these computational tasks defines heavy computations those expects large size data processing. Machine learning on classical space is very well set but it has more computational requirements based on complex and high-volume data processing. This paper surveys and propose model with integration of quantum computation and machine learning which will make sense on quantum machine learning concept. Quantum machine learning helps to enhance the various classical binary machine learning methods for better analysis and prediction of big data and information processing.KeywordsQubitQMLSupervised MLBloch SphereSuperpositionEntanglement

Background:Watermarking such as other security concepts is an ongoing challenging research issue, especially for medical images, to protect patient privacy. Medical images need to be shared and transferred between hospitals and specialists as quickly as possible for better diagnosis. Fast and simple watermarking is needed as well as the robust transferring of channel noise, such as salt and pepper noise and robust cropping that may occur from specialists and signature encryption for patient privacy.Methods:In this article, a highly robust and simple watermarking method is introduced. The proposed method has very low computational complexity and at the same time, it is very robust to interference and uses simple computations such as (XORs) Exclusive ORs and rotations that can be done in real-time. The proposed method uses a combination of hidden neighboring signature information, Sudoku permutation, and noise pre-processing to achieve high robustness against salt and pepper noise and cropping. Simple signature encryption is also used.Results:The proposed method is examined in different medical image datasets. The experimental results indicate the proposed watermarking system is robust to salt and pepper noise density of up to 90% and about 70% cropping. The number of computations including encryption is five XOR per pixel and a rotation per block of signature size.Conclusion:A novel method for medical image watermarking is presented. The proposed method is in the spatial domain, has encryption, and uses only XOR computation. The proposed method is highly robust to noise and cropping which is necessary for medical uses. The proposed method can be used efficiently for real-time watermarking for medical and nonmedical image datasets.

The rapid expansion of the fintech sector has brought with it an increasing demand for robust and sophisticated fraud detection systems capable of managing large volumes of financial transactions. Conventional machine learning (ML) approaches, while effective, often encounter limitations in terms of computational efficiency and the ability to model complex, high-dimensional data structures. Recent advancements in quantum computing have given rise to a promising paradigm known as quantum machine learning (QML), which leverages quantum mechanical principles to solve problems that are computationally infeasible for classical computers. The integration of QML with data science has opened new avenues for enhancing fraud detection frameworks by improving the accuracy and speed of transaction pattern analysis, anomaly detection, and risk mitigation strategies within fintech ecosystems. This paper aims to explore the potential of quantum-enhanced data science methodologies to bolster fraud detection and prevention mechanisms, providing a comparative analysis of QML techniques against classical ML models in the context of their application to financial data analysis. Fraud detection in fintech relies heavily on data-driven models to identify suspicious activities and prevent financial crimes such as identity theft, money laundering, and fraudulent transactions. Traditional ML approaches, such as decision trees, support vector machines, and deep learning, have laid the foundation for these systems. However, these approaches often fall short when faced with the challenges posed by high-dimensional, noisy, and complex financial data. Quantum machine learning, by leveraging quantum bits or qubits, possesses the unique ability to represent and process data in an exponentially larger state space, allowing for more efficient pattern recognition and computationally intensive analysis. Quantum algorithms such as the Quantum Support Vector Machine (QSVM), Quantum Principal Component Analysis (QPCA), and Quantum Neural Networks (QNNs) have been studied for their potential to outperform classical counterparts in specific problem domains, including fraud detection. This research delves into the theoretical foundations of quantum computing, outlining how quantum superposition, entanglement, and quantum interference can be harnessed to perform operations that exponentially accelerate data processing. Quantum algorithms are presented as capable of achieving faster data transformations and more nuanced pattern recognition through their ability to process all potential combinations of data simultaneously. The implementation of QML algorithms on quantum hardware, although still in its nascent stages, is beginning to demonstrate tangible benefits in terms of the speed and complexity of computations for fraud detection tasks. For example, quantum-enhanced anomaly detection can lead to the identification of rare, complex patterns that classical ML might overlook, contributing to a more proactive approach to fraud prevention. The paper also examines the integration of data science techniques with quantum-enhanced fraud detection, considering data preprocessing, feature engineering, and the application of quantum-enhanced statistical methods. Data preprocessing, a crucial step in building effective fraud detection models, involves the transformation and normalization of financial data to ensure that models can learn from relevant features without overfitting or underfitting. Quantum data structures offer the potential to represent data with a higher degree of complexity and interrelations, which is critical for capturing the multifaceted nature of financial transactions and detecting subtle signs of fraudulent activity. Quantum data encoding schemes such as Quantum Random Access Memory (QRAM) enable efficient storage and retrieval of data, providing a scalable solution for processing large datasets in real-time. A comprehensive analysis of case studies demonstrates the real-world applicability of quantum machine learning frameworks in fintech. The research highlights projects where quantum algorithms have been tested in controlled environments to detect anomalies in simulated transaction data, showcasing improvements in the identification of complex fraud scenarios over classical ML approaches. For instance, Quantum Support Vector Machines have been utilized to perform higher-dimensional classification tasks that are essential for distinguishing between legitimate and fraudulent transactions based on transaction history and user behavior. Furthermore, quantum algorithms that operate on hybrid systems, combining quantum and classical resources, are also explored to mitigate the limitations imposed by current quantum hardware, which is still constrained by issues such as noise and qubit coherence time. The paper also addresses key challenges and limitations associated with the integration of QML into practical fraud detection systems. Quantum hardware, although advancing rapidly, still faces significant challenges, including the need for error correction, qubit stability, and hardware scalability. Quantum computers with sufficient qubits and coherence time are necessary to implement complex algorithms for fraud detection effectively. Additionally, a practical approach to harnessing QML would require the development of quantum software frameworks and quantum programming languages that can operate in tandem with existing fintech systems and data infrastructure. Another area of focus is the synergy between quantum machine learning and classical machine learning models in creating hybrid systems that leverage the strengths of both methodologies. Quantum-enhanced feature extraction and dimensionality reduction can be combined with classical algorithms for final decision-making processes. This allows for a more comprehensive approach where quantum algorithms handle the computationally intensive parts of data analysis, while classical systems can be utilized for integrating real-time data and refining output for human interpretation. The paper discusses potential pathways for integrating these hybrid models, including considerations for API development, data interoperability, and the standardization of quantum-classical workflows. The discussion extends to the practical implications of implementing quantum-based fraud detection systems, particularly in terms of security and privacy. The use of quantum encryption and quantum key distribution can complement QML by ensuring that the data fed into fraud detection models is protected from external tampering. Quantum-resistant cryptography solutions are also explored, providing a comprehensive view of how quantum technologies could enhance the overall security posture of fintech ecosystems while promoting trust and compliance.

Quantum machine learning (QML) has emerged as a promising domain to leverage the computational capabilities of quantum systems to solve complex classification tasks. In this work, we present the first comprehensive QML study by benchmarking the MedMNIST-a diverse collection of medical imaging datasets on a 127-qubit real IBM quantum hardware, to evaluate the feasibility and performance of quantum models (without any classical neural networks) in practical applications. This study explores recent advancements in quantum computing such as device-aware quantum circuits, error suppression, and mitigation for medical image classification. The proposed methodology is comprised of three stages: preprocessing, generation of noise-resilient and hardware-efficient quantum circuits, optimizing/training of quantum circuits on classical hardware, and inference on real IBM quantum hardware. Firstly, we process all input images in the preprocessing stage to reduce the spatial dimension due to quantum hardware limitations. We generate hardware-efficient quantum circuits using backend properties expressible to learn complex patterns for medical image classification. After classical optimization of QML models, we perform inference on real quantum hardware. We also incorporate advanced error suppression and mitigation techniques in our QML workflow, including dynamical decoupling (DD), gate twirling (Twir), and matrix-free measurement mitigation (M3) to mitigate the effects of noise and improve classification performance. The experimental results showcase the potential of quantum computing for medical imaging and establish a benchmark for future advancements in QML applied to healthcare.

The early detection and accurate classification of diabetes health indicators are crucial for effective disease management and prevention. This study aims to compare the performance of classical and quantum machine learning models in feature selection and classification on the Diabetes Health Indicators dataset. Initially, classical machine learning methods were employed to preprocess the data, including normalization and scaling, followed by feature selection using Lasso regression. Various traditional models, such as Logistic Regression, Decision Trees, Random Forests, Gradient Boosting, K-Nearest Neighbors, and Naive Bayes, were evaluated. Among these, the Logistic Regression model achieved the highest accuracy at $85 \%$, while other models also demonstrated competitive performance with accuracies ranging from $82 \%$ to $85 \%$. Subsequently, quantum machine learning techniques were applied using the selected features to assess their effectiveness. Quantum circuits were created using Cirq, and parameter optimization was performed through Quantum Feature Mapping and Quantum Feature Transformation. The Quantum Support Vector Machine (QSVM) model attained an accuracy of $84.33 \%$, showing potential for matching the performance of traditional models. The results suggest that quantum machine learning models can offer comparable accuracy to classical methods in the classification of diabetes health indicators. This study highlights the potential benefits of integrating quantum techniques in complex data processing and recommends further exploration in future research to fully harness the capabilities of quantum machine learning.

Coronary heart disease (CHD) is a severe cardiac disease, and hence, its early diagnosis is essential as it improves treatment results and saves money on medical care. The prevailing development of quantum computing and machine learning (ML) technologies may bring practical improvement to the performance of CHD diagnosis. Quantum machine learning (QML) is receiving tremendous interest in various disciplines due to its higher performance and capabilities. Techniques for QML have the potential to forecast cardiac disease and help in early detection. To predict the risk of coronary heart disease, a hybrid approach utilising an ensemble machine learning model based on QML classifiers is presented in this paper. Our approach, with its unique ability to address multidimensional healthcare data, reassures the method’s robustness by fusing quantum and classical ML algorithms in a multi-step inferential framework. Reducing cardiac morbidity and mortality requires early detection of heart disease. In this research, a hybrid approach utilises techniques with quantum computing capabilities to tackle complex problems that are not amenable to conventional ML algorithms and to minimise computational expenses. The proposed method has been developed in the Raspberry Pi 4B Graphics Processing Unit (GPU) platform and tested on a broad dataset that integrates clinical and imaging data from patients suffering from CHD and healthy controls. The proposed research is developed with a hybrid approach that combines different machine learning algorithms, such as KNN + RF, DT + RF, LR + RF, and Adaboost + RF, for diagnosing coronary illness with higher accuracy through feature selection. The proposed system performance obtained an accuracy of 99%, utilising 20000 datasets with 14 attributes from various datasets collected from the Local Pathology Lab in the Muzaffarnagar District of Uttar Pradesh, India. Compared to classical machine learning models, the accuracy, sensitivity, F1 score, and specificity of the proposed hybrid QML model used with CHD are manifold higher.

Deep learning methods, especially convolutional neural networks, have become more and more popular in medical image classifications. However, training a deep neural network from scratch can be a luxury for many medical image datasets as the process requires a large and well-balanced sample to output satisfactory results. Unlike natural image datasets, medical images are expensive to collect owing to labor and equipment costs. Besides, the class labels in medical image datasets are usually severely imbalanced subject to the availability of patients. Further, aggregating medical images from multiple sources can be challenging due to policy restrictions, privacy concerns, communication costs, and data heterogeneity caused by equipment differences and labeling discrepancies. In this paper, we propose to address these issues with the help of transfer learning and artificial samples created by generative models. Instead of requesting medical images from source data, our method only needs a parsimonious supplement of model parameters pre-trained on the source data. The proposed method preserves the data privacy in the source data and significantly reduces the communication cost. Our study shows transfer learning together with artificial samples can improve the pneumonia classification accuracy on a small but heavily imbalanced chest X-ray image dataset by \(11.53\%\) which performs even better than directly augmenting that source data into the training process.KeywordsDeep learningGenerative modelsMedical image classificationPrivacy preservationTransfer learning

Quantum Machine Learning (QML), a confluence of quantum computing and classical machine learning, represents a revolutionary paradigm with transformative potential. This systematic review explores the landscape of QML by investigating its underlying principles, methodologies, diverse applications, challenges, and ethical considerations. Beginning with an examination of fundamental quantum computing principles, the review navigates through various QML methodologies, comparing them with classical counterparts. Real-world applications, ranging from quantum-enhanced optimization to drug discovery, are scrutinized, showcasing the practical implications of QML across industries. The paper systematically identifies challenges, including quantum hardware constraints and ethical considerations, while offering insights into current limitations and future research directions. A comparative analysis benchmarks QML against classical machine learning approaches, providing a nuanced understanding of its strengths and limitations. Ethical considerations underscore the importance of responsible AI practices in the integration of QML. The review concludes by identifying research gaps and suggesting future directions, emphasizing the need for continued exploration in this dynamic intersection of quantum computing and machine learning. This comprehensive exploration serves as a valuable resource for researchers, practitioners, and decision-makers seeking insights into the current state and transformative potential of Quantum Machine Learning.

At the intersection of machine learning and quantum computing, quantum machine learning has the potential of accelerating data analysis, especially for quantum data, with applications for quantum materials, biochemistry and high-energy physics. Nevertheless, challenges remain regarding the trainability of quantum machine learning models. Here we review current methods and applications for quantum machine learning. We highlight differences between quantum and classical machine learning, with a focus on quantum neural networks and quantum deep learning. Finally, we discuss opportunities for quantum advantage with quantum machine learning.

BackgroundThe need for better AI models fuels the demand for larger and larger high-quality datasets with significant diversity. Over the years, many medical imaging datasets have been published globally, but existing datasets do not contain enough samples from the population of the Indian subcontinent, leading to subpar performance of developed AI models when deployed in India. The Medical Imaging and Information Datasets (MIDAS) India initiative was launched to address this by developing standards, protocols, and policies for gathering medical imaging data nationwide.MethodsMIDAS employs a hub-and-spoke system for data collection, where each thematic hub works with a set of spokes to collect data for a specific disease or medical condition from primary, secondary, and tertiary health centers. The data gathering is guided by standard operating procedures developed from the collaborative efforts of the participating medical institutions. The annotation protocols are based on a combination of gold-standard tests and/or agreement between experts to achieve the required labeling accuracy, depending on the data type and the intended purpose of the dataset.ResultsThe MIDAS platform is accessible at https://midas.iisc.ac.in/. Two datasets are already available on MIDAS, one for oral cancer and another for dural-based pathologies, for free download. Many others are under development and review. Annotated and curated data are also available under various licenses as shared by the platform partners for the registered users. The datasets use standardized ontologies for annotations at both image and pixel-level regions of interest. The annotations undergo a review process before being published and accessible for download. Standards and guidelines for creating the datasets are evolving due to the complexity of the elements involved. Challenges are steeper, especially for data originating from early or pre-onset stages of diseases, such as dysplasia in oral cancer, where the manifestation of the disease feature(s) is sometimes unclear.ConclusionMIDAS India aims to catalyze the AI-driven transformation of healthcare by providing high-quality annotated imaging data tailored to local needs. It supports innovation, regulatory assessment, and clinical adoption of AI tools, serving as a scalable model for other countries looking to build similar data infrastructure to enhance digital healthcare delivery.

Convolutional Autoencoder-Based medical image compression using a novel annotated medical X-ray imaging dataset

In recent times, the combination of machine learning and quantum computing has been applied greatly to solve the problems of intelligent computing. The new emerging area of quantum machine learning can be exploited to great extent for accelerating the existing classical machine learning algorithms for accurate classifications and better predictions of massive data. Quantum computing and machine learning are anticipated to play a crucial part in how the community deals with information in the future. The objective of this chapter is to highlight the present evolution of quantum computers in the setting of intelligent data mining. Recent progress in quantum algorithms can act as a stepping stone for quantum machine learning models. The determination relative merits of classical and quantum machine learning models would depend on the current and future prospects of quantum computing. However, the implementation of quantum algorithms needs quantum hardware that is not yet accessible on a wide scale.

Deep convolutional neural networks (CNN) manifest the potential for computer-aided diagnosis systems (CADs) by learning features directly from images rather than using traditional feature extraction methods. Nevertheless, due to the limited sample sizes and heterogeneity in tumor presentation in medical images, CNN models suffer from training issues, including training from scratch, which leads to overfitting. Alternatively, a pre-trained neural network’s transfer learning (TL) is used to derive tumor knowledge from medical image datasets using CNN that were designed for non-medical activations, alleviating the need for large datasets. This study proposes two ensemble learning techniques: E-CNN (product rule) and E-CNN (majority voting). These techniques are based on the adaptation of the pretrained CNN models to classify colon cancer histopathology images into various classes. In these ensembles, the individuals are, initially, constructed by adapting pretrained DenseNet121, MobileNetV2, InceptionV3, and VGG16 models. The adaptation of these models is based on a block-wise fine-tuning policy, in which a set of dense and dropout layers of these pretrained models is joined to explore the variation in the histology images. Then, the models’ decisions are fused via product rule and majority voting aggregation methods. The proposed model was validated against the standard pretrained models and the most recent works on two publicly available benchmark colon histopathological image datasets: Stoean (357 images) and Kather colorectal histology (5,000 images). The results were 97.20% and 91.28% accurate, respectively. The achieved results outperformed the state-of-the-art studies and confirmed that the proposed E-CNNs could be extended to be used in various medical image applications.