Developing image analysis methods for digital pathology

The potential to use quantitative image analysis and artificial intelligence is one of the driving forces behind digital pathology. However, despite novel image analysis methods for pathology being described across many publications, few become widely adopted and many are not applied in more than a single study. The explanation is often straightforward: software implementing the method is simply not available, or is too complex, incomplete, or dataset‐dependent for others to use. The result is a disconnect between what seems already possible in digital pathology based upon the literature, and what actually is possible for anyone wishing to apply it using currently available software. This review begins by introducing the main approaches and techniques involved in analysing pathology images. I then examine the practical challenges inherent in taking algorithms beyond proof‐of‐concept, from both a user and developer perspective. I describe the need for a collaborative and multidisciplinary approach to developing and validating meaningful new algorithms, and argue that openness, implementation, and usability deserve more attention among digital pathology researchers. The review ends with a discussion about how digital pathology could benefit from interacting with and learning from the wider bioimage analysis community, particularly with regard to sharing data, software, and ideas. © 2022 The Author. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland

Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions

Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to improve the outcome of breast cancer patients. In the past decade, deep learning has shown remarkable progress in breast cancer imaging analysis, holding great promise in interpreting the rich information and complex context of breast imaging modalities. Considering the rapid improvement in the deep learning technology and the increasing severity of breast cancer, it is critical to summarize past progress and identify future challenges to be addressed. In this paper, we provide an extensive survey of deep learning-based breast cancer imaging research, covering studies on mammogram, ultrasound, magnetic resonance imaging, and digital pathology images over the past decade. The major deep learning methods, publicly available datasets, and applications on imaging-based screening, diagnosis, treatment response prediction, and prognosis are described in detail. Drawn from the findings of this survey, we present a comprehensive discussion of the challenges and potential avenues for future research in deep learning-based breast cancer imaging.Comment: Survey, 41 page

Applications of Artificial Intelligence in Biomedical Sciences

Η παρακάτω διπλωματική εργασία αποτελεί μια εκτεταμένη βιβλιογραφική ανασκόπηση των Εφαρμογών της Τεχνητής Νοημοσύνης στις Βιοϊατρικές Επιστήμες. Πιο αναλυτικά, εστιάζει στις εφαρμογές της Τεχνητής Νοημοσύνης στην Ανάπτυξη Νέων Φαρμάκων, στην Ανάλυση Εικόνων (Image Analysis), στην Ιατρική Φροντίδα (Healthcare), στα Radiomics και στις Κλινικές Δοκιμές (Clinical Trials). Η Τεχνητή Νοημοσύνη έχει αποτελέσει ακρογωνιαίο λίθο στην ανάπτυξη πολλών άλλων επιστημών και σύμφωνα με πλήθος ειδικών και ερευνητών θεωρείτο η μεγαλύτερη ανακάλυψη του αιώνα. Στο πρώτο κεφάλαιο αναλύεται η ιστορία της Τεχνητής Νοημοσύνης καθώς και ο ορισμός αυτής. Στο επόμενο κεφάλαιο γίνεται η αναζήτηση της βιβλιογραφίας στην οποία παρουσιάζονται τα πρώτα βήματα που έγιναν ώστε να δημιουργηθεί η επιστήμη που γνωρίζουμε σήμερα. Έπειτα αναλύονται οι εφαρμογές αυτής σε διάφορους τομείς καθώς και η συμβολή της στην περαιτέρω ανάπτυξη τους. Τέλος, στο τελευταίο κεφάλαιο, κεφάλαιο 4, γίνεται η συζήτηση πάνω σε ό,τι ειπώθηκε προηγουμένως καθώς και προτείνονται νέοι δρόμοι ανάπτυξης της επιστήμης της Τεχνητής Νοημοσύνης.In this dissertation is presented the contribution of AI in biomedical sciences and particularly in drug development, image analysis, healthcare, radiomics and clinical trials. It will be demonstrated the general theoretical context behind the evolution of Artificial Intelligence, as well as its applications. The first chapter, analyzes the history of Artificial Intelligence starting with its definition. The second chapter includes a review of the literature underlining some of the most important milestones of the creation of Artificial Intelligence. As AI has been conducive to the development of many fields it has been characterized by many experts as the biggest innovation of the century. Thus, the third chapter presents the different methods of machine learning used in those fields. In the last chapter of the thesis, chapter 4, is represented a discussion about the findings of the thesis as well as about some new ways that Artificial Intelligence could be beneficial

Applications of Artificial Intelligence in Battling Against Covid-19: A Literature Review

© 2020 Elsevier Ltd. All rights reserved.Colloquially known as coronavirus, the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), that causes CoronaVirus Disease 2019 (COVID-19), has become a matter of grave concern for every country around the world. The rapid growth of the pandemic has wreaked havoc and prompted the need for immediate reactions to curb the effects. To manage the problems, many research in a variety of area of science have started studying the issue. Artificial Intelligence is among the area of science that has found great applications in tackling the problem in many aspects. Here, we perform an overview on the applications of AI in a variety of fields including diagnosis of the disease via different types of tests and symptoms, monitoring patients, identifying severity of a patient, processing covid-19 related imaging tests, epidemiology, pharmaceutical studies, etc. The aim of this paper is to perform a comprehensive survey on the applications of AI in battling against the difficulties the outbreak has caused. Thus we cover every way that AI approaches have been employed and to cover all the research until the writing of this paper. We try organize the works in a way that overall picture is comprehensible. Such a picture, although full of details, is very helpful in understand where AI sits in current pandemonium. We also tried to conclude the paper with ideas on how the problems can be tackled in a better way and provide some suggestions for future works.Peer reviewe

Deep learning for accelerated magnetic resonance imaging

Medical imaging has aided the biggest advance in the medical domain in the last century. Whilst X-ray, CT, PET and ultrasound are a form of imaging that can be useful in particular scenarios, they each have disadvantages in cost, image quality, ease-of-use and ionising radiation. MRI is a slow imaging protocol which contributes to its high cost to run. However, MRI is a very versatile imaging protocol allowing images of varying contrast to be easily generated whilst not requiring the use of ionising radiation. If MRI can be made to be more efficient and smart, the effective cost of running MRI may be more affordable and accessible. The focus of this thesis is decreasing the acquisition time involved in MRI whilst maintaining the quality of the generated images and thus diagnosis. In particular, we focus on data-driven deep learning approaches that aid in the image reconstruction process and streamline the diagnostic process. We focus on three particular aspects of MR acquisition. Firstly, we investigate the use of motion estimation in the cine reconstruction process. Motion allows us to combine an abundance of imaging data in a learnt reconstruction model allowing acquisitions to be sped up by up to 50 times in extreme scenarios. Secondly, we investigate the possibility of using under-acquired MR data to generate smart diagnoses in the form of automated text reports. In particular, we investigate the possibility of skipping the imaging reconstruction phase altogether at inference time and instead, directly seek to generate radiological text reports for diffusion-weighted brain images in an effort to streamline the diagnostic process. Finally, we investigate the use of probabilistic modelling for MRI reconstruction without the use of fully-acquired data. In particular, we note that acquiring fully-acquired reference images in MRI can be difficult and nonetheless may still contain undesired artefacts that lead to degradation of the dataset and thus the training process. In this chapter, we investigate the possibility of performing reconstruction without fully-acquired references and furthermore discuss the possibility of generating higher quality outputs than that of the fully-acquired references.Open Acces

Image informatics approaches to advance cancer drug discovery

High content image-based screening assays utilise cell based models to extract and quantify morphological phenotypes induced by small molecules. The rich datasets produced can be used to identify lead compounds in drug discovery efforts, infer compound mechanism of action, or aid biological understanding with the use of tool compounds. Here I present my work developing and applying high-content image based screens of small molecules across a panel of eight genetically and morphologically distinct breast cancer cell lines. I implemented machine learning models to predict compound mechanism of action from morphological data and assessed how well these models transfer to unseen cell lines, comparing the use of numeric morphological features extracted using computer vision techniques against more modern convolutional neural networks acting on raw image data. The application of cell line panels have been widely used in pharmacogenomics in order to compare the sensitivity between genetically distinct cell lines to drug treatments and identify molecular biomarkers that predict response. I applied dimensional reduction techniques and distance metrics to develop a measure of differential morphological response between cell lines to small molecule treatment, which controls for the inherent morphological differences between untreated cell lines. These methods were then applied to a screen of 13,000 lead-like small molecules across the eight cell lines to identify compounds which produced distinct phenotypic responses between cell lines. Putative hits from a subset of approved compounds were then validated in a three-dimensional tumour spheroid assay to determine the functional effect of these compounds in more complex models, as well as proteomics to determine the responsible pathways. Using data generated from the compound screen, I carried out work towards integrating knowledge of chemical structures with morphological data to infer mechanistic information of the unannotated compounds, and assess structure activity relationships from cell-based imaging data

Deep Learning in Medical Image Analysis

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis