ENRICHing medical imaging training sets enables more efficient machine learning.

Anomaly detection (AD) is a challenging problem in computer vision. Particularly in the field of medical imaging, AD poses even more challenges due to a number of reasons, including insufficient availability of ground truth (annotated) data. In recent years, AD models based on generative adversarial networks (GANs) have made significant progress. However, their effectiveness in biomedical imaging remains underexplored. In this paper, we present an overview of using GANs for AD, as well as an investigation of state-of-the-art GAN-based AD methods for biomedical imaging and the challenges encountered in detail. We have also specifically investigated the advantages and limitations of AD methods on medical image datasets, conducting experiments using 3 AD methods on 7 medical imaging datasets from different modalities and organs/tissues. Given the highly different findings achieved across these experiments, we further analyzed the results from both data-centric and model-centric points of view. The results showed that none of the methods had reliable performance for detecting abnormalities in medical images. Factors such as the number of training samples, the subtlety of the anomaly, and the dispersion of the anomaly in the images are among the phenomena that highly impact the performance of the AD models. The obtained results were highly variable (AUC: 0.475-0.991; Sensitivity: 0.17-0.98; Specificity: 0.14-0.97). In addition, we provide recommendations for deployment of AD models in medical imaging and foresee important research directions.

Graph neural networks (GNNs) have achieved extraordinary enhancements in various areas including the fields medical imaging and network neuroscience where they displayed a high accuracy in diagnosing challenging neurological disorders such as autism. In the face of medical data scarcity and high-privacy, training such data-hungry models remains challenging. Federated learning brings an efficient solution to this issue by allowing to train models on multiple datasets, collected independently by different hospitals, in fully data-preserving manner. Although both state-of-the-art GNNs and federated learning techniques focus on boosting classification accuracy, they overlook a critical unsolved problem: investigating the reproducibility of the most discriminative biomarkers (i.e., features) selected by the GNN models within a federated learning paradigm. Quantifying the reproducibility of a predictive medical model against perturbations of training and testing data distributions presents one of the biggest hurdles to overcome in developing translational clinical applications. To the best of our knowledge, this presents the first work investigating the reproducibility of federated GNN models with application to classifying medical imaging and brain connectivity datasets. We evaluated our framework using various GNN models trained on medical imaging and connectomic datasets. More importantly, we showed that federated learning boosts both the accuracy and reproducibility of GNN models in such medical learning tasks. Our source code is available at https://github.com/basiralab/reproducibleFedGNN.KeywordsGraph neural networksFederated LearningReproducibilityBrain connectivity graphsPredictive medicine

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

Multimodal imaging is gaining in importance in the field of personalized medicine and can be described as a current trend in medical imaging diagnostics. The segmentation, classification and analysis of tissue structures is essential. The goal of this study is the evaluation of established segmentation methods on different medical image data sets acquired with different diagnostic procedures. Established segmentation methods were implemented using the latest state of the art and applied to medical image data sets. In order to benchmark the segmentation performance quantitatively, medical image data sets were superimposed with artificial Gaussian noise, and the mis-segmentation as a function of the image SNR or CNR was compared to a gold standard. The evaluation of the image segmentation showed that the best results of pixel-based segmentation (< 3%) can be achieved with methods of machine learning, multithreshold and advanced level-set method - even at high artificial noise (SNR< 18). Finally, the complexity of the object geometry and the contrast of the ROI to the surrounding tissue must also be considered to select the best segmentation algorithm.

The federated learning (FL) paradigm is well-suited for the field of medical image analysis, as it can effectively cope with machine learning on isolated multi-center data while protecting the privacy of participating parties. However, current research on optimization algorithms in FL often focuses on limited datasets and scenarios, primarily centered around natural images, with insufficient comparative experiments in medical contexts. In this work, we conduct a comprehensive evaluation of several state-of-the-art FL algorithms in the context of medical imaging. We conduct a fair comparison of classification models trained using various FL algorithms across multiple medical imaging datasets. Additionally, we evaluate system performance metrics, such as communication cost and computational efficiency, while considering different FL architectures. Our findings show that medical imaging datasets pose substantial challenges for current FL optimization algorithms. No single algorithm consistently delivers optimal performance across all medical FL scenarios, and many optimization algorithms may under-perform when applied to these datasets. Our experiments provide a benchmark and guidance for future research and application of FL in medical imaging contexts. Furthermore, we propose an efficient and robust method that combines generative techniques using denoising diffusion probabilistic models with label smoothing to augment datasets, widely enhancing the performance of FL on classification tasks across various medical imaging datasets.

The widespread applications of machine learning algorithms in high-stakes fields have demanded the development of effective explanation methods to help decision-makers interpret the black-box models. Post-hoc local feature attribution methods are among the most prevalent explainability approaches. These methods are often evaluated through human-subject studies, whose heterogeneous designs make results difficult to compare and sometimes unreliable. Although several quantitative evaluation metrics have been proposed, many suffer from different limitations, such as computational complexity and generalizability to complicated real-world datasets. Additionally, to our knowledge, there is no current work that establishes a comprehensive and reliable benchmark to evaluate all aspects of the methods in high-stakes settings. In this work, we introduce and assess a benchmark including a suite of quantitative metrics that provide a thorough evaluation of post-hoc feature attribution methods on 2 medical imaging datasets. We formalize 4 evaluation lenses, propose a new metric, and also apply a novel technique to prepare AI explanation artifacts for processing by explanation evaluation metrics. Our results show cross-dataset consistency, meaningful-random separation, sensitivity to model strength, and the value of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{4}$</tex> lenses. We provide reliable statistics that can guide the choice of explanation algorithms in medical imaging and beyond and help prioritize methods before investing in costly human-subject experiments.

Multimodal medical image fusion plays an important role in clinical applications. However, multimodal medical image fusion methods ignore the feature dependence among modals, and the feature fusion ability with different granularity is not strong. A Long-Range Correlation-Guided Dual-Encoder Fusion Network for Medical Images is proposed in this paper. The main innovations of this paper are as follows: Firstly, A Cross-dimension Multi-scale Feature Extraction Module (CMFEM) is designed in the encoder, by extracting multi-scale features and aggregating coarse-to-fine features, the model realizes fine-grained feature enhancement in different modalities. Secondly, a Long-range Correlation Fusion Module (LCFM) is designed, by calculating the long-range correlation coefficient between local features and global features, the same granularity features are fused by the long-range correlation fusion module. long-range dependencies between modalities are captured by the model, and different granularity features are aggregated. Finally, this paper is validated on clinical multimodal lung medical image dataset and brain medical data dataset. On the lung medical image dataset, IE, AG, {textbf {Q}}^{{textbf {AB/F}}}, and EI metrics are improved by 4.53%, 4.10%, 6.19%, and 6.62% respectively. On the brain medical image dataset, SF, VIF, and {textbf {Q}}^{{textbf {AB/F}}} metrics are improved by 3.88%, 15.71%, and 7.99% respectively. This model realizes better fusion performance, which plays an important role in the fusion of multimodal medical images.

Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis.

Magnetic resonance imaging (MRI) is a substantial imaging procedure for diagnosing brain tumors. However, brain tumor classification continues challenging due to the unequal distribution of classes within datasets, complicating precise diagnosis and classification. This research focuses on the class imbalance in medical image datasets by proposing a hybrid bio-inspired algorithm for brain tumor classification. A rider optimization and particle rider mutual information-based dendritic-squirrel search algorithm combined with an artificial immune classifier is developed and tested on imbalanced datasets generated from BRATS and SimBRATS. Experimental outcomes are encouraging, For the imbalanced BRATS dataset, the rider optimization- based classifier achieved an accuracy of 94.84%, sensitivity of 92.96%, and specificity of 94.95%. The particle rider mutual information-based classifier outperformed others with 96.25% accuracy, 94.33% sensitivity, and 94.85% specificity. For the imbalanced SimBRATS dataset, the rider optimization-based classifier achieved 94.95% accuracy, 92.05% sensitivity, and 94.04% specificity. The particle rider mutual information-based classifier excelled with 96.35% accuracy, 94.42% sensitivity, and 95.44% specificity. These findings suggest that the proposed algorithm effectively addresses class imbalance in medical image datasets, offering a robust solution for brain tumor classification. The particle rider mutual information-based classifier shows promise for enhancing diagnostic accuracy in clinical settings, demonstrating the efficacy of hybridized bio-inspired algorithms in managing imbalanced datasets.

The batch effect is a nonbiological variation that arises from technical differences across different batches of data during the data generation process for acquisition-related reasons, such as collection of images at different sites or using different scanners. This phenomenon can affect the robustness and generalizability of computational pathology- or radiology-based cancer diagnostic models, especially in multicenter studies. To address this issue, we developed an open-source platform, Batch Effect Explorer (BEEx), that is designed to qualitatively and quantitatively determine whether batch effects exist among medical image datasets from different sites. A suite of tools was incorporated into BEEx that provide visualization and quantitative metrics based on intensity, gradient, and texture features to allow users to determine whether there are any image variables or combinations of variables that can distinguish datasets from different sites in an unsupervised manner. BEEx was designed to support various medical imaging techniques, including microscopy and radiology. Four use cases clearly demonstrated the ability of BEEx to identify batch effects and validated the effectiveness of rectification methods for batch effect reduction. Overall, BEEx is a scalable and versatile framework designed to read, process, and analyze a wide range of medical images to facilitate the identification and mitigation of batch effects, which can enhance the reliability and validity of image-based studies. Significance: BEEx is a prescreening tool for image-based analyses that allows researchers to evaluate batch effects in multicenter studies and determine their origin and magnitude to facilitate development of accurate AI-based cancer models.

Abstract: A brain tumour is described by the presence of abnormal cells in the brain's tissues. Brain tumours can be benign (not cancerous) or malignant (cancerous). The malignant brain tumour is one of the leading and common cancers in the world. There are two types of tumours, primary tumours that develop in the brain and secondary tumours that start in another region of the body and then spread to the brain. The precise identification of the size and location of a brain tumour is crucial in the diagnosis of a brain tumour and is often diagnosed with magnetic resonance imaging (MRI). This book chapter discusses the major types of brain tumours and the advancements in computeraided approaches for detecting brain tumours. The manuscript gives an overview of various recent machine learning and medical image processing approaches developed recently for the identification and classification of brain tumours. Several medical image dataset available for the research works in this domain is also briefed in this article. The major research challenges which can be addressed by the researchers in the domain of brain tumour detection are also discussed in this article. Method: This manuscript mainly discusses various medical image processing methods to detect brain tumors. Results: The manuscript discusses the efficiency of the existing schemes and the key areas where further improvement is required Conclusion: This manuscript gives an overview of various categories of brain tumors, existing methods to detect brain tumors, the critical challenges in this domain and the medical image dataset available for the study.

U-Net convolutional neural networks have become a cornerstone in medical image processing, particularly for complex segmentation tasks. However, with the proliferation of various U-Net variants, it is imperative to evaluate their performance across diverse medical datasets to determine the most suitable architecture for specific applications. This study presents a comprehensive empirical analysis of six U-Net variants namely; U-Net, U-Net++, ResU-Net, TransU-Net, V-Net, and U-Net 3+, focusing on their effectiveness in medical image segmentation. The research used two publicly available medical image datasets: the Breast Cancer Ultrasound dataset and the Brain MRI FLAIR dataset. These datasets were selected to represent a range of challenges in medical imaging, including varying levels of noise, contrast, color channels, and lesion visibility. Each U-Net variant was trained and evaluated under consistent experimental conditions, with the Dice Similarity Coefficient (DSC), Jaccard Index and accuracy serving as the primary evaluation metrics. The findings reveal that while all U-Net variants exhibit strong performance, ResU-Net consistently outperformed other architectures across both datasets. Specifically, ResU-Net achieved the highest Jaccard Index and DSC values, recording 0.5158 and 0.5930 for the Breast Cancer Ultrasound dataset and 0.7923 and 0.8730 for the Brain MRI dataset, respectively. Additionally, ResU-Net demonstrated the highest accuracy of 96.20% in the Breast Ultrasound dataset and emerged as the first runner-up in the Brain MRI dataset with an accuracy of 99.70%, trailing slightly behind U-Net++, which achieved 97.72%. Conversely, U-Net 3+ registered the lowest performance in both datasets. ResU-Net's superior performance suggests that its residual connections enhance feature propagation and mitigate the vanishing gradient problem, leading to better segmentation outcomes. Conversely, U-Net 3+ showed lower performance, indicating that complexity doesn’t always equate to better results. This study offers insights into the strengths and weaknesses of different U-Net variants, guiding practitioners in choosing the most suitable model for their specific applications especially in medical image processing domain.

Interactive segmentation methods utilize user-provided positive and negative clicks to guide the model in accurately segmenting target objects. Compared to fully automatic medical image segmentation, these methods can achieve higher segmentation accuracy with limited image data, demonstrating significant potential in clinical applications. Typically, for each new click provided by the user, conventional interactive segmentation methods reprocess the entire network by re-inputting the click into the segmentation model, which greatly increases the user’s interaction burden and deviates from the intended goal of interactive segmentation tasks. To address this issue, we propose an efficient segmentation network, ESM-Net, for interactive medical image segmentation. It obtains high-quality segmentation masks based on the user’s initial clicks, reducing the complexity of subsequent refinement steps. Recent studies have demonstrated the strong performance of the Mamba model in various vision tasks; however, its application in interactive segmentation remains unexplored. In our study, we incorporate the Mamba module into our framework for the first time and enhance its spatial representation capabilities by developing a Spatial Augmented Convolution (SAC) module. These components are combined as the fundamental building blocks of our network. Furthermore, we designed a novel and efficient segmentation head to fuse multi-scale features extracted from the encoder, optimizing the generation of the predicted segmentation masks. Through comprehensive experiments, our method achieved state-of-the-art performance on three medical image datasets. Specifically, we achieved 1.43 NoC@90 on the Kvasir-SEG dataset, 1.57 NoC@90 on the CVC-ClinicDB polyp segmentation dataset, and 1.03 NoC@90 on the ADAM retinal disk segmentation dataset. The assessments on these three medical image datasets highlight the effectiveness of our approach in interactive medical image segmentation.

The protection of medical images against unauthorized access and tampering is paramount. This paper presents a robust watermarking framework that integrates Discrete Wavelet Transform (DWT), Hessenberg Decomposition (HMD), Singular Value Decomposition (SVD), and Arnold Scrambling to enhance the security of medical images. By applying DWT to decompose the medical image into frequency subbands and embedding the watermark into the most significant subband, the proposed algorithm ensures minimal impact on image quality. HMD simplifies the subband matrix, while SVD extracts and manipulates the essential features of the image. Arnold Scrambling is employed to further secure the watermark image before embedding. Experimental results on various medical image datasets demonstrate the algorithm’s effectiveness in maintaining imperceptibility, with a peak signal-to-noise ratio (PSNR) of up to 49 dB, and robustness against common image processing attacks, such as compression and noise addition. The proposed scheme achieves a balance between imperceptibility and robustness, making it suitable for securing medical images in digital environments. The proposed scheme has been implemented on different medical datasets and the performance is evaluated in terms of its imperceptibility and robustness. The PSNR value achieved by the proposed work is 49 dB which proves that the embedded watermark image is imperceptible while the NC value achieved is higher than 0.9 against most of the attacks, hence proves its robustness against multiple attacks.

Deep learning (DL) approaches have been applied in different sectors of medical imaging applications, i.e. classification, segmentation and detection tasks and shown superior performance. The DL based generative methods are used for image denoising, enhancement and restoration task. In case of image analysis, image denoising is one of the most crucial preprocessing steps. Recently, there are various DL approaches are applied in image denoising problems and achieved state-of-the-art performance. In this work, we apply recurrent residual U-Net (R2U-Net) based autoencoder model for medical image denoising which is applied for digital pathology, dermoscopy, Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) images denoising tasks. The performance of R2U-Net based autoencoder model is also evaluated for Transfer domain (TD) between MRI and CT scan images. The experiments have conducted on different publicly available medical image datasets and shows promising denoising results which can be applied in different medical imaging applications.