Epithelial segmentation from in situ hybridisation histological samples using a deep central attention learning approach

The assessment of pathological samples by molecular techniques, such as in situ hybridization (ISH) and immunohistochemistry (IHC), has revolutionised modern Histopathology. Most often it is important to detect ISH/IHC reaction products in certain cells or tissue types. For instance, detection of human papilloma virus (HPV) in oropharyngeal cancer samples by ISH products is difficult and remains a tedious and time consuming task for experts. Here we introduce a proposed framework to segment epithelial regions in oropharyngeal tissue images with ISH staining. First, we use colour deconvolution to obtain a counterstain channel and generate input patches based on superpixels and their neighbouring areas. Then, a novel deep attention residual network is applied to identify the epithelial regions to produce an epithelium segmentation mask. In the experimental results, comparing the proposed network with other state-of-the-art deep learning approaches, our network provides a better performance than region-based and pixel-based segmentations

AI-driven sentiment analysis on servitization business model: A Twitter Based Study

In today's dynamic market landscape, servitization, the transformation of products into services, has emerged as a prominent strategy for businesses to enhance value proposition and foster long-term customer relationships. Understanding public perception and sentiment towards servitization is crucial for companies aiming to capitalize on this paradigm shift. This research leverages natural language processing (NLP) techniques, social media platforms and machine learning technology to gain a deeper understanding of public opinion on servitization, thereby informing strategic decision-making and enhancing competitive advantage in the marketplace. In particular, we propose an AI-driven sentiment analysis technique to automatically categorize a given Twitter post as positive, negative, or neutral about servitization. To this end, we collected more 100,000 tweets related to servitization, in two different periods (March 2023 and February 2024). We used 12 targeted keywords and hashtags, such as Servitization, Productasaservice, and Serviceselling. Leveraging NLP techniques, we preprocessed the collected data to remove noise, tokenize text, and extract relevant features, leaving a total of 73,549 preprocessed tweets for further analysis. The manual annotation of such large data was extremely difficult, for this we used a popular sentiment annotation method, namely RoBERTa (A Robustly Optimized BERT Pretraining Approach), to calculate the intensity of the sentiment as either positive, neutral, or negative. The labelled tweets were then used to train a series of machine learning and deep learning algorithms to automatically detect the sentiment of social media tweets in relation to servitization. To this end, we experimented with popular machine learning algorithms including Naïve Byte, Logical Regression, and Stochastic Gradient Descent classifiers, providing accuracies of 66%, 78 %, and 75%, respectively. Furthermore, we evaluated the performance of three deep learning methods, namely Long-Short Term Memory (LSTM), Bidirectional-LSTM (Bi-LSTM), and Hybrid Bi-LSTM, providing accuracies of 80%, 79%, and 79 %, respectively. Our results revealed that LSTM outperformed all the studied algorithms in classifying tweets into positive, negative, or neutral sentiments. Our research utilizes topic modeling techniques to identify key themes within the positive, negative and neutral tweets related to servitization. This allowed us to gain deeper insights into the drivers and barriers influencing public perception of servitization. Results revealed that people's attitude in response to service offerings on the Twitter platform is generally positive, with percentage of 47%. Our analysis indicates that positive tweets predominantly focus on topics related to time optimization, use technology, and service accessibility. This suggests that users perceive service-based products, aided by technology, as efficient time-savers and more accessible alternatives compared to traditional product purchases. Additionally, positive tweets comprised topics related to sales offers and discounts on services, indicating a positive association between cost-saving opportunities and user satisfaction. On the other hand, negative tweets (32% of data) highlighted keywords such as "expensive," "cancel," "remind," and "monthly subscription." These findings suggest that negative sentiment is often linked to concerns about high costs, subscription-related issues, and reminders, potentially indicating dissatisfaction with pricing models, subscription plans or billing practices. Our work highlights the role of NLP and machine learning technologies in extracting and analysing social media content related to servitization to extract valuable insights for businesses seeking to gauge market sentiment and tailor servitization offerings to meet customer needs and preferences. Our results reveal that while users generally embrace the convenience and accessibility of service offerings, concerns about cost and subscription terms can lead to negative sentiment among users

Novel applications of discrete mereotopology to mathematical morphology

This paper shows how the Discrete Mereotopology notions of adjacency and neighbourhood between regions can be exploited through Mathematical Morphology to accept or reject changes resulting from traditional morphological operations such as closing and opening. This leads to a set of six morphological operations (here referred to generically as minimal opening and minimal closing ) where minimal changes fulfil specific spatial constraints. We also present an algorithm to compute the RCC5D and RCC8D relation sets across multiple regions resulting in a performance improvement of over three orders of magnitude over our previously published algorithm for Discrete Mereotopology

Mereotopological Correction of Segmentation Errors in Histological Imaging

In this paper we describe mereotopological methods to programmatically correct image segmentation errors, in particular those that fail to fulfil expected spatial relations in digitised histological scenes. The proposed approach exploits a spatial logic called discrete mereotopology to integrate a number of qualitative spatial reasoning and constraint satisfaction methods into imaging procedures. Eight mereotopological relations defined on binary region pairs are represented as nodes in a set of 20 directed graphs, where the node-to-node graph edges encode the possible transitions between the spatial relations after set-theoretic and discrete topological operations on the regions are applied. The graphs allow one to identify sequences of operations that applied to regions of a given relation, and enables one to resegment an image that fails to conform to a valid histological model into one that does. Examples of the methods are presented using images of H&E-stained human carcinoma cell line cultures

A review of natural language processing in contact centre automation

Contact centres have been highly valued by organizations for a long time. However, the COVID-19 pandemic has highlighted their critical importance in ensuring business continuity, economic activity, and quality customer support. The pandemic has led to an increase in customer inquiries related to payment extensions, cancellations, and stock inquiries, each with varying degrees of urgency. To address this challenge, organizations have taken the opportunity to re-evaluate the function of contact centres and explore innovative solutions. Next-generation platforms that incorporate machine learning techniques and natural language processing, such as self-service voice portals and chatbots, are being implemented to enhance customer service. These platforms offer robust features that equip customer agents with the necessary tools to provide exceptional customer support. Through an extensive review of existing literature, this paper aims to uncover research gaps and explore the advantages of transitioning to a contact centre that utilizes natural language solutions as the norm. Additionally, we will examine the major challenges faced by contact centre organizations and offer reco

Epithelium and Stroma Identification in Histopathological Images using Unsupervised and Semi-supervised Superpixel-based Segmentation

We present superpixel-based segmentation frameworks for unsupervised and semi-supervised epithelium-stroma identification in histopathological images or oropharyngeal tissue micro arrays. A superpixel segmentation algorithm is initially used to split-up the image into binary regions (superpixels) and their colour features are extracted and fed into several base clustering algorithms with various parameter initializations. Two Consensus Clustering (CC) formulations are then used: the Evidence Accumulation Clustering (EAC) and the voting-based consensus function. These combine the base clustering outcomes to obtain a more robust detection of tissue compartments than the base clustering methods on their own. For the voting-based function, a technique is introduced to generate consistent labellings across the base clustering results. The obtained CC result is then utilized to build a self-training Semi-Supervised Classification (SSC) model. Unlike supervised segmentations, which rely on large number of labelled training images, our SSC approach performs a quality segmentation while relying on few labelled samples. Experiments conducted on forty-five hand-annotated images of oropharyngeal cancer tissue microarrays show that (a) the CC algorithm generates more accurate and stable results than individual clustering algorithms; (b) the clustering performance of the voting-based function outperforms the existing EAC; and (c) the proposed SSC algorithm outperforms the supervised methods, which is trained with only a few labelled instances

Human papilloma virus detection in oropharyngeal carcinomas with in situ hybridisation using hand crafted morphological features and deep central attention residual networks

Human Papilloma Virus (HPV) is a major risk factor for the development of oropharyngeal cancer. Automatic detection of HPV in digitized pathology tissues using \textit{in situ} hybridisation (ISH) is a difficult task due to the variability and complexity of staining patterns as well as the presence of imaging and staining artefacts. This paper proposes an intelligent image analysis framework to determine HPV status in digitized samples of oropharyngeal cancer tissue micro-arrays (TMA). The proposed pipeline mixes handcrafted feature extraction with a deep learning for epithelial region segmentation as a preliminary step. We apply a deep central attention learning technique to segment epithelial regions and within those assess the presence of regions representing ISH products. We then extract relevant morphological measurements from those regions which are then input into a supervised learning model for the identification of HPV status. The performance of the proposed method has been evaluated on 2,009 TMA images of oropharyngeal carcinoma tissues captured with a $\times$20 objective. The experimental results show that our technique provides around 91\% classification accuracy in detecting HPV status when compared with the histopatholgist gold standard. We also tested the performance of end-to-end deep learning classification methods to assess HPV status by learning directly from the original ISH processed images, rather than from the handcrafted features extracted from the segmented images. We examined the performance of sequential convolutional neural networks (CNN) architectures including {three popular image recognition networks (VGG-16, ResNet and Inception V3) in their pre-trained and trained from scratch versions, however their highest classification accuracy was inferior (78\%) to the hybrid pipeline presented here}

Classifying Cognitive Profiles Using Machine Learning with Privileged Information in Mild Cognitive Impairment

Early diagnosis of dementia is critical for assessing disease progression and potential treatment. State-or-the-art machine learning techniques have been increasingly employed to take on this diagnostic task. In this study, we employed Generalised Matrix Learning Vector Quantization (GMLVQ) classifiers to discriminate patients with Mild Cognitive Impairment (MCI) from healthy controls based on their cognitive skills. Further, we adopted a ``Learning with privileged information'' approach to combine cognitive and fMRI data for the classification task. The resulting classifier operates solely on the cognitive data while it incorporates the fMRI data as privileged information (PI) during training. This novel classifier is of practical use as the collection of brain imaging data is not always possible with patients and older participants.MCI patients and healthy age-matched controls were trained to extract structure from temporal sequences. We ask whether machine learning classifiers can be used to discriminate patients from controls based on the learning performance and whether differences between these groups relate to individual cognitive profiles. To this end, we tested participants in four cognitive tasks: working memory, cognitive inhibition, divided attention, and selective attention. We also collected fMRI data before and after training on the learning task and extracted fMRI responses and connectivity as features for machine learning classifiers. Our results show that the PI guided GMLVQ classifiers outperform the baseline classifier that only used the cognitive data. In addition, we found that for the baseline classifier, divided attention is the only relevant cognitive feature. When PI was incorporated, divided attention remained the most relevant feature while cognitive inhibition became also relevant for the task. Interestingly, this analysis for the fMRI GMLVQ classifier suggests that (1) when overall fMRI signal for structured stimuli is used as inputs to the classifier, the post-training session is most relevant; and (2) when the graph feature reflecting underlying spatiotemporal fMRI pattern is used, the pre-training session is most relevant. Taken together these results suggest that brain connectivity before training and overall fMRI signal after training are both diagnostic of cognitive skills in MCI

Analysis of clinical and virologic features in Hepatitis B e Antigen (HbeAg)-negative and HbeAg-positive Egyptian chronic hepatitis B patients

Background: HBeAg\u2013negative chronic hepatitis B infection has a divergent clinical course from that of HBeAg-positive infection. Objectives: To analyze the frequency and to compare the different features of HBeAg-negative and HBeAg-positive chronic hepatitis B patients. Methods: One hundred and twenty one Egyptian patients with chronic hepatitis B (CHB), underwent laboratory investigations and transient elastography (TE). Comparisons according to HBeAg status were conducted regarding their demographic, liver biochemical and virologic characters. Results: 97 patients (80.2%) were HBeAg-negative while 24 patients (19.8%) were HBeAg-positive. HBeAg-negative patients were significantly older in age than CHBeAg-positive patients (p=0.001). ALT levels in HBeAg-negative patients were significantly lower than those in HBeAg-positive patients (p=0.02), whereas serum albumin was lower in the HBeAg-positive group (p=0.03). The percentage of HBV DNA higher than 20000 IU/mL in HBeAg-negative patients was lower than those in HBeAg-positive patients (p=0.24). Stages of fibrosis by TE showed that 30.9% of HBeAg-negative and 41.7% of HBeAg-positive had a fibrosis score >F2. Four patients (3.3%) were diagnosed with HCC; all of whom were HBeAg-negative. Conclusion: HBeAg-negative patients compared with HBeAg-positive patients had older age, lower ALT and serum HBVDNA levels, but more incidence of HCC