Image Analysis Solutions Research Articles

Актуальні проблеми цифрової обробки трихологічних зображень

This article explores modern methods of digital processing of trichological images for hair condition diagnosis. Special attention is paid to modules that employ computer vision techniques and machine learning algorithms to enhance diagnostic accuracy. Proposed automated image analysis solutions effectively detect hair structure anomalies and simplify the diagnostic process. The article discusses the advantages and limitations of existing approaches and prospects for their further development to improve medical procedures. Applications of these technologies in clinical practice are also examined.

Advanced petrographic thin section segmentation through deep learning-integrated adaptive GLFIF

In geological research, precise segmentation of sandstone thin sections is crucial for detailed subsurface material analysis. Traditional methods often fall short in accurately capturing the complexities of these samples. This study presents an innovative segmentation approach that integrates an adaptive Global and Local Fuzzy Image Fitting (GLFIF) algorithm with Otsu's thresholding, significantly enhancing segmentation accuracy and efficiency. Our method combines deep learning and traditional image processing techniques. The adaptive GLFIF algorithm, powered by deep learning, automates parameter tuning, thereby reducing manual intervention and improving precision. Unlike conventional methods that learn fixed parameters, our model dynamically adjusts the segmentation process to achieve accurate results. The dual-phase segmentation strategy effectively isolates small features and handles intricate boundaries, ensuring high-quality outcomes. Experimental results demonstrate that our approach improves segmentation accuracy by 11.2% (from 82.6% to 93.8%), the Jaccard index by 15.4% (from 76.8% to 92.2%), and the Dice coefficient by 9% (from 86.9% to 95.9%) compared to traditional methods. This technique bridges the gap between conventional image analysis and deep learning, combining precise segmentation with the automation and computational power of advanced algorithms. Our segmentation algorithm represents a significant advancement in automated petrographic thin section analysis. Traditional image processing methods, such as thresholding and level sets, excel in handling small objects and complex boundaries but require significant manual intervention and cannot achieve full automation. Recent deep learning methods, particularly semantic segmentation, offer end-to-end automation but struggle with small targets and intricate boundaries. Our approach effectively combines the strengths of both methodologies, providing a comprehensive and efficient solution for geological image analysis that ensures both high accuracy and full automation.

Trans-DCN: A High-Efficiency and Adaptive Deep Network for Bridge Cable Surface Defect Segmentation

Cables are vital load-bearing components of cable-stayed bridges. Surface defects can lead to internal corrosion and fracturing, significantly impacting the stability of the bridge structure. The detection of surface defects from bridge cable images faces numerous challenges, including shadow disturbances due to uneven lighting and difficulties in addressing multiscale defect features. To address these challenges, this paper proposes a novel and cost-effective deep learning segmentation network, named Trans-DCN, to detect defects in the surface of the bridge cable. The network leverages an efficient Transformer-based encoder and integrates multiscale features to overcome the limitations associated with local feature inadequacy. The decoder implements an atrous Deformable Convolution (DCN) pyramid and dynamically fuses low-level feature information to perceive the complex distribution of defects. The effectiveness of Trans-DCN is evaluated by comparing it with state-of-the-art segmentation baseline models using a dataset comprising cable bridge defect images. Experimental results demonstrate that our network outperforms the state-of-the-art network SegFormer, achieving a 27.1% reduction in GFLOPs, a 1.2% increase in mean Intersection over Union, and a 1.5% increase in the F1 score. Ablation experiments confirmed the effectiveness of each module within our network, further substantiating the significant validity and advantages of Trans-DCN in the task of bridge cable defect segmentation. The network proposed in this paper provides an effective solution for downstream cable bridge image analysis.

Classification of Cardiovascular Arrhythmia Using Deep Learning Techniques: A Review

Deep Learning (DL), an offshoot of Machine Learning (ML) has emerged as a powerful and feasible solution for medical image analysis due to advancements in robust computer software and hardware technologies. It plays a key role in Cardiovascular disease (CVD) diagnosis by detecting anomalies in Electrocardiogram (ECG) signals. Cardiac arrhythmia, which refers to irregular heartbeat, may signal an early symptom of CVD and can lead to fatal outcomes if ignored. Accurate detection of arrhythmia is very challenging even for experts to distinguish between acute and chronic conditions in ECG readings. This triggered the focus of researchers to explore the application of Artificial Intelligence for ECG classification. Traditional machine learning methods use handcrafted features that require domain knowledge. The new era in DL makes the automatic detection of Cardiovascular Disease (CVD) possible. In this paper, an exhaustive review of DL-based techniques for ECG classification has been presented. Research findings in this survey indicate the challenges and issues with arrhythmia detection, such as single lead and multiple lead ECG signals, choice of the size of the training data set, and the number of arrhythmia classes, etc. The study also signifies that there is great scope for improving the performance of arrhythmia prediction models by employing hybrid ensemble learning, time series analysis using Recurrent Neural Network architectures and identification of unexplored classes of arrhythmia.

A Review of Nuclei Detection and Segmentation on Microscopy Images Using Deep Learning With Applications to Unbiased Stereology Counting.

The detection and segmentation of stained cells and nuclei are essential prerequisites for subsequent quantitative research for many diseases. Recently, deep learning has shown strong performance in many computer vision problems, including solutions for medical image analysis. Furthermore, accurate stereological quantification of microscopic structures in stained tissue sections plays a critical role in understanding human diseases and developing safe and effective treatments. In this article, we review the most recent deep learning approaches for cell (nuclei) detection and segmentation in cancer and Alzheimer's disease with an emphasis on deep learning approaches combined with unbiased stereology. Major challenges include accurate and reproducible cell detection and segmentation of microscopic images from stained sections. Finally, we discuss potential improvements and future trends in deep learning applied to cell detection and segmentation.

Innovative Deep Learning Approach for Parkinson's Disease Prediction: Leveraging Convolutional Neural Networks for Early Detection

INTRODUCTION: Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting movement control, highlighting the importance of timely detection and intervention to improve patient quality of life. However, accurate diagnosis remains challenging due to its similarity with other neurological conditions, leading to a 25% rate of inaccurate manual diagnoses. Convolutional Neural Networks (CNNs) offer a promising solution for medical image classification and analysis, capable of learning complex patterns in images. In this study, we introduce an innovative automated diagnostic model using CNN that gives an appropriate output about if the person is diagnosed with PD or not.OBJECTIVES: The study aims to develop an automated diagnostic model using CNNs to accurately diagnose PD. By leveraging the Parkinson Progression Markers Initiative (PPMI) dataset, which provides benchmarked MRI images of PD and healthy controls, the model seeks to differentiate between PD and non-PD cases.METHODS: A Convolutional Neural Network (CNN) is a deep learning algorithm that is suitable for medical image classification and analysis as they are able to learn complex patterns in images and identify the hidden patterns and trend of data. We have used VGG16 and ResNet50 pretrained CNN models to achieve high accuracy and prediction.RESULTS: These models collectively achieved an outstanding accuracy rate of 97%. To validate our model performance, we test our model by applying various algorithms and activation functions such as EfficientNetB0, EfficientNetB1 and softmax, sigmoid, and ReLu respectively.CONCLUSION: This research introduces an innovative framework for the early detection of Parkinson’s disease using convolutional neural networks. Our system demonstrates remarkable capability to identify subtle patterns indicative of PD in its early stages.

LitefusionNet: Boosting the performance for medical image classification with an intelligent and lightweight feature fusion network

Medical image analysis plays a crucial role in modern healthcare for accurate diagnosis and treatment. However, the inherent challenges and limitations posed by the complexity and variability of medical images, coupled with the shortcomings of existing methods, necessitate the development of novel approaches. In this study, we propose LiteFusionNet (Lightweight Fusion Network), a lightweight model that effectively addresses these challenges, offering the advantage of accurate and efficient medical image classification while mitigating the computational demands. The LitefusionNet leverages the power of deep convolutional neural networks (DCNNs) and feature fusion techniques to achieve improved performance in medical image classification. LitefusionNet combines the strengths of MobileNet and MobileNetV2 architectures to extract robust features from medical images. These features capture discriminative information from different levels of abstraction, enhancing the model's ability to capture fine-grained patterns. The fusion process employs a concatenation method to combine the extracted features, resulting in a more comprehensive representation that improves the model's classification accuracy. To evaluate the effectiveness of LitefusionNet, extensive experiments are conducted on a diverse set of publicly available medical image datasets, including brain MRI, skin, CT, X-ray, and histology. The results demonstrate that LitefusionNet outperforms several existing models in terms of classification accuracy, showcasing its efficacy in different medical imaging modalities. Furthermore, we provide interpretability to the model's predictions by performing Grad-CAM analysis, enabling insights into the important regions in the medical images that contribute to the classification decision. In addition, we compare LitefusionNet with five pre-trained models. LiteFusionNet excels in medical image classification, boasting impressive accuracies across diverse datasets: 97.33% for brain MRI, 91.11% for skin, 99.00% for CT, 98.15% for X-ray, and 92.11% for histology. These results underscore LiteFusionNet's robust and versatile performance, making it a compelling solution for accurate and efficient medical image analysis. Overall, LitefusionNet strikes a balance between accuracy, efficiency, and real-time performance. Our findings demonstrate its potential as a promising solution for accurate and efficient medical image analysis, with applications in diagnostic support systems and clinical decision-making.

The Crucial Role of Interdisciplinary Conferences in Advancing Explainable AI in Healthcare

As artificial intelligence (AI) integrates within the intersecting domains of healthcare and computational biology, developing interpretable models tailored to medical contexts is met with significant challenges. Explainable AI (XAI) is vital for fostering trust and enabling effective use of AI in healthcare, particularly in image-based specialties such as pathology and radiology where adjunctive AI solutions for diagnostic image analysis are increasingly utilized. Overcoming these challenges necessitates interdisciplinary collaboration, essential for advancing XAI to enhance patient care. This commentary underscores the critical role of interdisciplinary conferences in promoting the necessary cross-disciplinary exchange for XAI innovation. A literature review was conducted to identify key challenges, best practices, and case studies related to interdisciplinary collaboration for XAI in healthcare. The distinctive contributions of specialized conferences in fostering dialogue, driving innovation, and influencing research directions were scrutinized. Best practices and recommendations for fostering collaboration, organizing conferences, and achieving targeted XAI solutions were adapted from the literature. By enabling crucial collaborative junctures that drive XAI progress, interdisciplinary conferences integrate diverse insights to produce new ideas, identify knowledge gaps, crystallize solutions, and spur long-term partnerships that generate high-impact research. Thoughtful structuring of these events, such as including sessions focused on theoretical foundations, real-world applications, and standardized evaluation, along with ample networking opportunities, is key to directing varied expertise toward overcoming core challenges. Successful collaborations depend on building mutual understanding and respect, clear communication, defined roles, and a shared commitment to the ethical development of robust, interpretable models. Specialized conferences are essential to shape the future of explainable AI and computational biology, contributing to improved patient outcomes and healthcare innovations. Recognizing the catalytic power of this collaborative model is key to accelerating the innovation and implementation of interpretable AI in medicine.

Image Segmentation Based on G.O.A for Finding Deformities in Medical and Aura Images

Objectives: The research aims to develop the segmentation model to identify the deformity in the medical images as accurately as possible and plan for better medical treatment. The study is extended to identify the disease before its appearance in the human body through human aura images to support aura imaging in medical diagnosis. Methods: The study used a brain image from the UCI data set and Aura images from the Biowell data set to identify the disease. The segmentation model Bivariate Gaussian Mixture Model (B.G.M.M) was developed. Model parameters are derived using the Expectation Maximization (E.M) Algorithm. The Grasshopper optimization Algorithm (G.O.A) extracts optimal features from the images. The chosen feature is fed as input to the classification model B.G.M.M. Segmentation accuracy is measured using the quality metrics. Findings: The developed approach shows 97% accuracy in identifying the damaged tissues in MRI images and high-intensity energy zones in the aura images, indicating the potential for deformities. Novelty: This study significantly contributes to the field by offering novel solutions for precise and comprehensive image analysis in medical and aura imaging contexts. Keywords: G.O.A, segmentation, G.M.M, E.M, quality metrics, deformity identification, Hue and saturation

ADVANCED ELECTRON MICROSCOPY IMAGE PROCESSING FOR ANALYZING AMORPHOUS ALLOYS: ELECTRON MICROSCOPY IMAGE CLUSTER ANALYZER (EMICA). TOOL AND RESULTS

This article unveils EMICA, a Python-based software tool revolutionizing electron microscopy image processing for amorphous alloys. EMICA addresses the unique challenges posed by these materials, which lack long-range order, by providing specialized capabilities for cluster analysis and spatial pattern recognition. This research explored software tool development and application through illustrative examples, answering the key question of how they enhance amorphous alloy analysis. By integrating advanced image processing techniques and algorithms, EMICA uncovers hidden patterns, offering quantitative insights into cluster distributions. The key message emphasizes the application's transformative impact on material science research, providing a specialized solution for electron microscopy image analysis in the amorphous alloy domain. Our key findings, presented through real-world examples and case studies, attest to the efficacy of the software in revealing nuanced details of amorphous alloy structures. From identifying subtle variations in atomic configurations to quantifying cluster distributions, EMICA represents a significant leap forward in the field of advanced electron microscopy image processing, contributing significantly to the advancement of this domain.

Abstract 2504: Determining tumour homologous recombination repair status in PDX and clinical samples using automated RAD51 scoring

Abstract PARP inhibitors (PARPi) as monotherapy are approved in a wide variety of cancers that show genetic alterations that result in homologous recombination repair (HRR) deficiency. Current genomic scar tests for HRR deficiency (HRD) are predictive of response to PARPi on a population scale but they may fail to provide a real-time functional status of HRR in an individual. RAD51 is a critical DNA repair factor that functions in HRR to safeguard the integrity of the genome and could be a useful patient selection biomarker for PARPi response.During HRR, RAD51 forms filamentous structures to facilitate repair, which can be visualised by direct immunofluorescence (IF) as nuclear foci. Quantification of these RAD51 foci has been shown to be a functional readout of HRR status, however current manual counting of foci is challenging and can result in high inter-observer variability. In order to standardise RAD51 quantification, we aimed to develop an image analysis solution.Tumour samples from patient derived xenograft (PDX) studies and human tissues from commercial sources and clinical trials were co-stained with IF for RAD51 and geminin, as a marker of cells in S/G2 phase of the cell cycle. Manual scoring was performed as reported previously. Visiopharm was used to create and image analysis pipeline in allowing delineate tumour epithelial areas, detection of the geminin positive nuclei and RAD51 foci within these and the quantification of the foci.We have successfully developed an image analysis solution to select geminin positive proliferating tumour cells from PDX and clinical material, then quantify the RAD51 foci within these cells. There is a good correlation with manual scores with a similar accuracy to the manual inter-observer variability (PDX: R2 = 0.76, F1 = 0.95 [n=34]; breast cancer samples: R2 = 0.72, F1 = 0.92 [n=33]). Developing and image analysis solution has challenges due to several factors such as pan-nuclear staining of non-focus forming RAD51 being variable across samples, manual scoring using a wider Z-plane, susceptibility of RAD51 to pre-analytical factors and the fact that a duplex IF stain with geminin is required to mark actively replicating cells.The frequent inactivation of HRR is known to sensitise cancer cells to PARP inhibitors, we demonstrate a quantitative functional readout of HRR competency using RAD51 foci staining and an image analysis algorithm. This solution may enable the use of RAD51 foci as a patient selection biomarker for PARP inhibitors by providing an objective, robust and reproducible algorithm for scoring. Citation Format: Jack Robertson, Jack Ashton, Gemma N. Jones, Gordon Mills, Tugba Y. Ozmen, Matthew J. Rames, Soonyoung Park, Paul Waring, Natalia Lukashchuk, Elizabeth Harrington. Determining tumour homologous recombination repair status in PDX and clinical samples using automated RAD51 scoring [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2504.

Automating the Human Action of First-Trimester Biometry Measurement from Real-World Freehand Ultrasound

Objective: Automated medical image analysis solutions should closely mimic complete human actions to be useful in clinical practice. However, more often an automated image analysis solution only represents part of a human task which restricts its practical utility. In the case of ultrasound-based fetal biometry, an automated solution should ideally recognize key fetal structures in free-hand video guidance, select a standard plane from a video stream and perform biometry. A complete automated solution should automate all three sub-actions.Methods: This paper considers how to automate the complete human action of first-trimester biometry measurement from real-world freehand ultrasound. In the proposed hybrid Convolutional Neural Network (CNN) architecture design, a classification-regression-based guidance model detects and tracks fetal anatomical structures (using visual cues) in the ultrasound video. Several high-quality standard planes which contain the mid-sagittal view of the fetus are sampled at multiple timestamps (using a custom-designed confident-frame detector) based on the estimated probability values associated with predicted anatomical structures that define the biometry plane. Automated semantic segmentation is performed on the selected frames to extract fetal anatomical landmarks. A crown-rump length (CRL) estimate is calculated as the mean CRL from these multiple frames.Results: Our fully automated method shows a high correlation with clinical expert CRL measurement (Pearson ρ=0.92, R-Squared (R2)=0.84) and a low mean absolute error of 0.834 (weeks) for fetal age estimation on a test data set of 42 videos.Conclusion: A novel algorithm for standard plane detection employs a quality detection mechanism defined by clinical standards and ensuring precise biometric measurements.

Organ-Agnostic Whole Slide Image Analysis using Self-Supervised Transfer Learning

Traditional methods for pathology image analysis are well-known for their time-consuming and labor-intensive nature, often relying on the expertise of pathologists. In recent years, numerous research studies have been proposed to develop automated systems using machine learning approaches to address these challenges. While these systems have demonstrated promising performance, they often exhibit bias towards specific organs, cells, or tasks, limiting their ability to provide generalized solutions for pathology image analysis. To address this issue, I propose an organ-agnostic pathology image analysis system that leverages a self-supervised transfer learning approach. The proposed system comprises two stages: self-supervised representation learning and transfer learning. In the self-supervised representation learning phase, a machine learning model is trained to consistently extract essential features encapsulating the characteristics of diverse pathological images such as visual patterns of tumors. Subsequently, in the transfer learning phase, these well-pretrained models are utilized to train downstream tasks, such as tumor type classification or cancer area segmentation. The proposed approach outperforms all existing state-of-the-art supervised methods in multiple public pathology image benchmarks.

Robust Object Segmentation using 3D Mesh Models and Self-Supervised Learning

The proposed method presents an innovative solution that addresses the challenges of unlabeled data, robust performance in unknown environments, and foreground-background differentiation in object segmentation. The objective of this work is to improve object segmentation tasks with an unannotated pool of data, derive model structure to demystify and educate large AI models by using knowledge distillation method, implementing framework with self-supervised learning analyse impacting and driving factors. Proposed method handles unlabeled data effectively, improving accuracy and generalization in diverse scenarios. It demonstrates remarkable reliability in unknown environments, ensuring consistent and accurate object segmentation in complex visual contexts. By incorporating novel techniques, the approach overcomes the longstanding problem of achieving consistent and non-trivial solutions for object segmentation, offering a comprehensive and effective solution for computer vision and image analysis. The literature review reveals that the DINO model using a ViT-B/8 backbone achieves an impressive 80.1% accuracy in linear regression, while the DINO model with a ViT-B/16 backbone attains the highest k-NN accuracy at 78.3%. The approach integrates deep neural networks for object segmentation with Kalman filtering for non-linear state estimation. Specifically, depth information obtained from point clouds is directly incorporated into the Unscented Kalman filtering, allowing for efficient and accurate performance. A one-parameter procedure is employed to identify and eliminate irregularities in point clouds, significantly improving the performance of segmenting objects. By incorporating self-supervised learning with distillation, the method effectively predicts and addresses the challenge of refining trivial solutions, leading to improved performance in viewpoint and illumination scenarios. Decision-making is deferred until a global view of the entire frame can be established, enabling accurate and consistent object segmentation in subsequent frames. The proposed method, SSL-DM+UKF, outperforms existing approaches in various challenges, including occlusion, viewpoint changes, and illumination variations. It achieves statistical superiority over the baseline SSL-Distillation Model by up to 2.50% in different scenarios. Incorporating depth observations through the Unscented Kalman Filter and using self-supervised learning with knowledge distillation are key factors contributing to its success. SSL-DM+UKF offers a valuable and efficient solution for real-time object segmentation in dynamic environments.

XAI Applications in Medical Imaging: A Survey of Methods and Challenges

Medical imaging plays a pivotal role in modern healthcare, aiding in the diagnosis, monitoring, and treatment of various medical conditions. With the advent of Artificial Intelligence (AI), medical imaging has witnessed remarkable advancements, promising more accurate and efficient analysis. However, the black-box nature of many AI models used in medical imaging has raised concerns regarding their interpretability and trustworthiness. In response to these challenges, Explainable AI (XAI) has emerged as a critical field, aiming to provide transparent and interpretable solutions for medical image analysis. This survey paper comprehensively explores the methods and challenges associated with XAI applications in medical imaging. The survey begins with an introduction to the significance of XAI in medical imaging, emphasizing the need for transparent and interpretable AI solutions in healthcare. We delve into the background of medical imaging in healthcare and discuss the increasing role of AI in this domain. The paper then presents a detailed survey of various XAI techniques, ranging from interpretable machine learning models to deep learning approaches with built-in interpretability and post hoc interpretation methods. Furthermore, the survey outlines a wide range of applications where XAI is making a substantial impact, including disease diagnosis and detection, medical image segmentation, radiology reports, surgical planning, and telemedicine. Real-world case studies illustrate successful applications of XAI in medical imaging. The challenges associated with implementing XAI in medical imaging are thoroughly examined, addressing issues related to data quality, ethics, regulation, clinical integration, model robustness, and human-AI interaction. The survey concludes by discussing emerging trends and future directions in the field, highlighting the ongoing efforts to enhance XAI methods for medical imaging and the critical role XAI will play in the future of healthcare. This survey paper serves as a comprehensive resource for researchers, clinicians, and policymakers interested in the integration of Explainable AI into medical imaging, providing insights into the latest methods, successful applications, and the challenges that lie ahead.

A Comprehensive Survey of Convolutional Neural Networks for Skin Cancer Classification and Prediction

Skin cancer, a prevalent and potentially fatal condition, requires early detection and precise classification to ensure effective treatment. In recent years, there has been a significant rise in the popularity of Convolutional Neural Networks (CNNs) prominence as a robust solution for image processing and analysis, significantly surpassing conventional techniques in skin cancer prediction and classification. This survey paper offers a thorough examination of CNNs and their diverse applications in diagnosing skin cancer, emphasizing their benefits, existing obstacles, and potential avenues for future research.

Seismic Image Identification and Detection Based on Tchebichef Moment Invariant

The research focuses on the analysis of seismic data, specifically targeting the detection, edge segmentation, and classification of seismic images. These processes are fundamental in image processing and are crucial in understanding the stratigraphic structure and identifying oil and natural gas resources. However, there is a lack of sufficient resources in the field of seismic image detection, and interpreting 2D seismic image slices based on 3D seismic data sets can be challenging. In this research, image segmentation involves image preprocessing and the use of a U-net network. Preprocessing techniques, such as Gaussian filter and anisotropic diffusion, are employed to reduce blur and noise in seismic images. The U-net network, based on the Canny descriptor is used for segmentation. For image classification, the ResNet-50 and Inception-v3 models are applied to classify different types of seismic images. In image detection, Tchebichef invariants are computed using the Tchebichef polynomials’ recurrence relation. These invariants are then used in an optimized multi-class SVM network for detecting and classifying various types of seismic images. The promising results of the SVM model based on Tchebichef invariants suggest its potential to replace Hu moment invariants (HMIs) and Zernike moment invariants (ZMIs) for seismic image detection. This approach offers a more efficient and dependable solution for seismic image analysis in the future.

SUFI: an automated approach to spectral unmixing of fluorescent multiplex images captured in mouse and post-mortem human brain tissues

BackgroundMultispectral fluorescence imaging coupled with linear unmixing is a form of image data collection and analysis that allows for measuring multiple molecular signals in a single biological sample. Multiple fluorescent dyes, each measuring a unique molecule, are simultaneously measured and subsequently “unmixed” to provide a read-out for each molecular signal. This strategy allows for measuring highly multiplexed signals in a single data capture session, such as multiple proteins or RNAs in tissue slices or cultured cells, but can often result in mixed signals and bleed-through problems across dyes. Existing spectral unmixing algorithms are not optimized for challenging biological specimens such as post-mortem human brain tissue, and often require manual intervention to extract spectral signatures. We therefore developed an intuitive, automated, and flexible package called SUFI: spectral unmixing of fluorescent images.ResultsThis package unmixes multispectral fluorescence images by automating the extraction of spectral signatures using vertex component analysis, and then performs one of three unmixing algorithms derived from remote sensing. We evaluate these remote sensing algorithms’ performances on four unique biological datasets and compare the results to unmixing results obtained using ZEN Black software (Zeiss). We lastly integrate our unmixing pipeline into the computational tool dotdotdot, which is used to quantify individual RNA transcripts at single cell resolution in intact tissues and perform differential expression analysis, and thereby provide an end-to-end solution for multispectral fluorescence image analysis and quantification.ConclusionsIn summary, we provide a robust, automated pipeline to assist biologists with improved spectral unmixing of multispectral fluorescence images.

Integrating Network Clustering Analysis and Computational Methods to Understand Communication With and About Brands: Opportunities and Challenges

Brand-related content cocreated by consumers can play a crucial role in brand–consumer interactions and provide brands with valuable insights hidden in vast seas of unstructured data. We propose and evaluate a framework integrating a social network approach and scalable automated content analysis of texts and visuals for studying brand-related communication on social media. To illustrate the proposed approach, we use Twitter content related to two brands: Barclays and Sierra Club. By applying network clustering algorithms we identify different types of organically emerging communities around brands. Cluster-specific diffusion leaders are identified using their in-degree centrality values. To examine the unique characteristics of brand-related content within each cluster, we apply and assess the accuracy of popular off-the-shelf solutions for text and image analysis, also known as application programming interfaces (APIs). Of six sentiment analysis solutions, only one shows acceptable reliability levels. For computer vision APIs, we first identify labels that have unclear or imprecise meaning and calculate accuracy levels, resulting in acceptable accuracy levels for four of the five APIs. We discuss conceptual and practical implications of this integrative approach and of the technological hurdles that these popular automated content analysis applications pose.

Gamma function based ensemble of CNN models for breast cancer detection in histopathology images

Breast cancer is the second deadliest disease amongst women worldwide. Breast histopathology image analysis is one of the most powerful ways used for the detection of tumour malignancies. Manual breast histopathology image analysis is, however, subjective, time-consuming and prone to human errors. Computer-aided diagnosis (CAD) has become a popular and viable solution for medical image analysis due to recent advances in computer power and memory. However, the performance of the CAD models needs to be improved to use for practical purposes. Convolutional neural network (CNN) based models have achieved promising results for breast histopathological image classification. In this paper, instead of relying on a single CNN model, we have proposed a novel rank-based ensemble method by combining outcomes of three transfer learning CNN models, namely GoogleNet, VGG11 and MobileNetV3_Small. The proposed ensemble model is designed using the Gamma function for solving a 2-class classification problem of breast histopathological images. In comparison to state-of-the-art approaches, our method produces better classification results, with 99.16%, 98.24%, 98.67%, and 96.16% for 40X, 100X, 200X, and 400X levels of magnification, respectively, on a publicly accessible standard dataset called BreakHis and 96.95% on another well-known dataset called ICIAR-2018.