Digital Pathology: The Time Is Now to Bridge the Gap between Medicine and Technological Singularity

Digitalization of the imaging in radiology is a reality in several healthcare institutions worldwide. The challenges of filing, confidentiality, and manipulation have been brilliantly solved in radiology. However, digitalization of hematoxylin- and eosin-stained routine histological slides has shown slow movement. Although the application for external quality assurance is a reality for a pathologist with most of the continuing medical education programs utilizing virtual microscopy, the abandonment of traditional glass slides for routine diagnostics is far from the perspectives of many departments of laboratory medicine and pathology. Digital pathology images are captured as images by scanning and whole slide imaging/virtual microscopy can be obtained by microscopy (robotic) on an entire histological (microscopic) glass slide. Since 1986, services using telepathology for the transfer of images of anatomic pathology between detached locations have benefited countless patients globally, including the University of Alberta. The purpose of specialist recertification or re-validation for the Royal College of Pathologists of Canada belonging to the Royal College of Physicians and Surgeons of Canada and College of American Pathologists is a milestone in virtual reality. Challenges, such as high bandwidth requirement, electronic platforms, the stability of the operating systems, have been targeted and are improving enormously. The encryption of digital images may be a requirement for the accreditation of laboratory services—quantum computing results in quantum-mechanical phenomena, such as superposition and entanglement. Different from binary digital electronic computers based on transistors where data are encoded into binary digits (bits) with two different states (0 and 1), quantum computing uses quantum bits (qubits), which can be in superpositions of states. The use of quantum computing protocols on encrypted data is crucial for the permanent implementation of virtual pathology in hospitals and universities. Quantum computing may well represent the technological singularity to create new classifications and taxonomic rules in medicine

Added benefits of computer-assisted analysis of Hematoxylin-Eosin stained breast histopathological digital slides

This thesis aims at determining if computer-assisted analysis can be used to better understand pathologists’ perception of mitotic figures on Hematoxylin-Eosin (HE) stained breast histopathological digital slides. It also explores the feasibility of reproducible histologic nuclear atypia scoring by incorporating computer-assisted analysis to cytological scores given by a pathologist. In addition, this thesis investigates the possibility of computer-assisted diagnosis for categorizing HE breast images into different subtypes of cancer or benign masses. In the first study, a data set of 453 mitoses and 265 miscounted non-mitoses within breast cancer digital slides were considered. Different features were extracted from the objects in different channels of eight colour spaces. The findings from the first research study suggested that computer-aided image analysis can provide a better understanding of image-related features related to discrepancies among pathologists in recognition of mitoses. Two tasks done routinely by the pathologists are making diagnosis and grading the breast cancer. In the second study, a new tool for reproducible nuclear atypia scoring in breast cancer histological images was proposed. The third study proposed and tested MuDeRN (MUlti-category classification of breast histopathological image using DEep Residual Networks), which is a framework for classifying hematoxylin-eosin stained breast digital slides either as benign or cancer, and then categorizing cancer and benign cases into four different subtypes each. The studies indicated that computer-assisted analysis can aid in both nuclear grading (COMPASS) and breast cancer diagnosis (MuDeRN). The results could be used to improve current status of breast cancer prognosis estimation through reducing the inter-pathologist disagreement in counting mitotic figures and reproducible nuclear grading. It can also improve providing a second opinion to the pathologist for making a diagnosis

On Edge Computing for Remote Pathology Consultations and Computations

Telepathology aims to replace the pathology operations performed on-site, but current systems are limited by their prohibitive cost, or by the adopted underlying technologies. In this work, we contribute to overcoming these limitations by bringing the recent advances of edge computing to reduce latency and increase local computation abilities to the pathology ecosystem. In particular, this paper presents LiveMicro, a system whose benefit is twofold: on one hand, it enables edge computing driven digital pathology computations, such as data-driven image processing on a live capture of the microscope. On the other hand, our system allows remote pathologists to diagnosis in collaboration in a single virtual microscope session, facilitating continuous medical education and remote consultation, crucial for under-served and remote hospital or private practice. Our results show the benefits and the principles underpinning our solution, with particular emphasis on how the pathologists interact with our application. Additionally, we developed simple yet effective diagnosis-aided algorithms to demonstrate the practicality of our approach

Enhancing Breast Cancer Prediction Using Unlabeled Data

Selles väitekirjas esitatakse sildistamata andmeid kasutav süvaõppe lähenemine rinna infiltratiivse duktaalse kartsinoomi koeregioonide automaatseks klassifitseerimiseks rinnavähi patoloogilistes digipreparaatides. Süvaõppe meetodite tööpõhimõte on sarnane inimajule, mis töötab samuti mitmetel tõlgendustasanditel. Need meetodid on osutunud tulemuslikeks ka väga keerukate probleemide nagu pildiliigituse ja esemetuvastuse lahendamisel, ületades seejuures varasemate lahendusviiside efektiivsust. Süvaõppeks on aga vaja suurt hulka sildistatud andmeid, mida võib olla keeruline saada, eriti veel meditsiinis, kuna nii haiglad kui ka patsiendid ei pruugi olla nõus sedavõrd delikaatset teavet loovutama. Lisaks sellele on masinõppesüsteemide saavutatavate aina paremate tulemuste hinnaks nende süsteemide sisemise keerukuse kasv. Selle sisemise keerukuse tõttu muutub raskemaks ka nende süsteemide töö mõistmine, mistõttu kasutajad ei kipu neid usaldama. Meditsiinilisi diagnoose ei saa järgida pimesi, kuna see võib endaga kaasa tuua patsiendi tervise kahjustamise. Mudeli mõistetavuse tagamine on seega oluline viis süsteemi usaldatavuse tõstmiseks, eriti just masinõppel põhinevate mudelite laialdasel rakendamisel sellistel kriitilise tähtsusega aladel nagu seda on meditsiin. Infiltratiivne duktaalne kartsinoom on üks levinumaid ja ka agressiivsemaid rinnavähi vorme, moodustades peaaegu 80% kõigist juhtumitest. Selle diagnoosimine on patoloogidele väga keerukas ja ajakulukas ülesanne, kuna nõuab võimalike pahaloomuliste kasvajate avastamiseks paljude healoomuliste piirkondade uurimist. Samas on infiltratiivse duktaalse kartsinoomi digipatoloogias täpne piiritlemine vähi agressiivsuse hindamise aspektist ülimalt oluline. Käesolevas uurimuses kasutatakse konvolutsioonilist närvivõrku arendamaks välja infiltratiivse duktaalse kartsinoomi diagnoosimisel rakendatav pooleldi juhitud õppe skeem. Välja pakutud raamistik suurendab esmalt väikest sildistatud andmete hulka generatiivse võistlusliku võrgu loodud sünteetiliste meditsiiniliste kujutistega. Seejärel kasutatakse juba eelnevalt treenitud võrku, et selle suurendatud andmekogumi peal läbi viia kujutuvastus, misjärel sildistamata andmed sildistatakse andmesildistusalgoritmiga. Töötluse tulemusena saadud sildistatud andmeid eelmainitud konvolutsioonilisse närvivõrku sisestades saavutatakse rahuldav tulemus: ROC kõvera alla jääv pindala ja F1 skoor on vastavalt 0.86 ja 0.77. Lisaks sellele võimaldavad välja pakutud mõistetavuse tõstmise tehnikad näha ka meditsiinilistele prognooside otsuse tegemise protsessi seletust, mis omakorda teeb süsteemi usaldamise kasutajatele lihtsamaks. Käesolev uurimus näitab, et konvolutsioonilise närvivõrgu tehtud otsuseid aitab paremini mõista see, kui kasutajatele visualiseeritakse konkreetse juhtumi puhul infiltratiivse duktaalse kartsinoomi positiivse või negatiivse otsuse langetamisel süsteemi jaoks kõige olulisemaks osutunud piirkondi.The following thesis presents a deep learning (DL) approach for automatic classification of invasive ductal carcinoma (IDC) tissue regions in whole slide images (WSI) of breast cancer (BC) using unlabeled data. DL methods are similar to the way the human brain works across different interpretation levels. These techniques have shown to outperform traditional approaches of the most complex problems such as image classification and object detection. However, DL requires a broad set of labeled data that is difficult to obtain, especially in the medical field as neither the hospitals nor the patients are willing to reveal such sensitive information. Moreover, machine learning (ML) systems are achieving better performance at the cost of becoming increasingly complex. Because of that, they become less interpretable that causes distrust from the users. Model interpretability is a way to enhance trust in a system. It is a very desirable property, especially crucial with the pervasive adoption of ML-based models in the critical domains like the medical field. With medical diagnostics, the predictions cannot be blindly followed as it may result in harm to the patient. IDC is one of the most common and aggressive subtypes of all breast cancers accounting nearly 80% of them. Assessment of the disease is a very time-consuming and challenging task for pathologists, as it involves scanning large swatches of benign regions to identify an area of malignancy. Meanwhile, accurate delineation of IDC in WSI is crucial for the estimation of grading cancer aggressiveness. In the following study, a semi-supervised learning (SSL) scheme is developed using the deep convolutional neural network (CNN) for IDC diagnosis. The proposed framework first augments a small set of labeled data with synthetic medical images, generated by the generative adversarial network (GAN) that is followed by feature extraction using already pre-trained network on the larger dataset and a data labeling algorithm that labels a much broader set of unlabeled data. After feeding the newly labeled set into the proposed CNN model, acceptable performance is achieved: the AUC and the F-measure accounting for 0.86, 0.77, respectively. Moreover, proposed interpretability techniques produce explanations for medical predictions and build trust in the presented CNN. The following study demonstrates that it is possible to enable a better understanding of the CNN decisions by visualizing areas that are the most important for a particular prediction and by finding elements that are the reasons for IDC, Non-IDC decisions made by the network

The Devil is in the Details: Whole Slide Image Acquisition and Processing for Artifacts Detection, Color Variation, and Data Augmentation: A Review

Whole Slide Images (WSI) are widely used in histopathology for research and the diagnosis of different types of cancer. The preparation and digitization of histological tissues leads to the introduction of artifacts and variations that need to be addressed before the tissues are analyzed. WSI preprocessing can significantly improve the performance of computational pathology systems and is often used to facilitate human or machine analysis. Color preprocessing techniques are frequently mentioned in the literature, while other areas are usually ignored. In this paper, we present a detailed study of the state-of-the-art in three different areas of WSI preprocessing: Artifacts detection, color variation, and the emerging field of pathology-specific data augmentation. We include a summary of evaluation techniques along with a discussion of possible limitations and future research directions for new methods.European Commission 860627Ministerio de Ciencia e Innovacion (MCIN)/Agencia Estatal de Investigacion (AEI) PID2019-105142RB-C22Fondo Europeo de Desarrollo Regional (FEDER)/Junta de Andalucia-Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades B-TIC-324-UGR20Instituto de Salud Carlos III Spanish Government European Commission BES-2017-08158

A survey on artificial intelligence in histopathology image analysis

The increasing adoption of the whole slide image (WSI) technology in histopathology has dramatically transformed pathologists' workflow and allowed the use of computer systems in histopathology analysis. Extensive research in Artificial Intelligence (AI) with a huge progress has been conducted resulting in efficient, effective, and robust algorithms for several applications including cancer diagnosis, prognosis, and treatment. These algorithms offer highly accurate predictions but lack transparency, understandability, and actionability. Thus, explainable artificial intelligence (XAI) techniques are needed not only to understand the mechanism behind the decisions made by AI methods and increase user trust but also to broaden the use of AI algorithms in the clinical setting. From the survey of over 150 papers, we explore different AI algorithms that have been applied and contributed to the histopathology image analysis workflow. We first address the workflow of the histopathological process. We present an overview of various learning-based, XAI, and actionable techniques relevant to deep learning methods in histopathological imaging. We also address the evaluation of XAI methods and the need to ensure their reliability on the field