Classification and Retrieval of Digital Pathology Scans: A New Dataset

In this paper, we introduce a new dataset, \textbf{Kimia Path24}, for image classification and retrieval in digital pathology. We use the whole scan images of 24 different tissue textures to generate 1,325 test patches of size 1000$\times$1000 (0.5mm$\times$0.5mm). Training data can be generated according to preferences of algorithm designer and can range from approximately 27,000 to over 50,000 patches if the preset parameters are adopted. We propose a compound patch-and-scan accuracy measurement that makes achieving high accuracies quite challenging. In addition, we set the benchmarking line by applying LBP, dictionary approach and convolutional neural nets (CNNs) and report their results. The highest accuracy was 41.80\% for CNN.Comment: Accepted for presentation at Workshop for Computer Vision for Microscopy Image Analysis (CVMI 2017) @ CVPR 2017, Honolulu, Hawai

Assisted Diagnosis of Cervical Intraepithelial Neoplasia (CIN)

This paper introduces an automated computer- assisted system for the diagnosis of cervical intraepithelial neoplasia (CIN) using ultra-large cervical histological digital slides. The system contains two parts: the segmentation of squamous epithelium and the diagnosis of CIN. For the segmentation, to reduce processing time, a multiresolution method is developed. The squamous epithelium layer is first segmented at a low (2X) resolution. The boundaries are further fine tuned at a higher (20X) resolution. The block-based segmentation method uses robust texture feature vectors in combination with support vector machines (SVMs) to perform classification. Medical rules are finally applied. In testing, segmentation using 31 digital slides achieves 94.25% accuracy. For the diagnosis of CIN, changes in nuclei structure and morphology along lines perpendicular to the main axis of the squamous epithelium are quantified and classified. Using multi-category SVM, perpendicular lines are classified into Normal, CIN I, CIN II, and CIN III. The robustness of the system in term of regional diagnosis is measured against pathologists' diagnoses and inter-observer variability between two pathologists is considered. Initial results suggest that the system has potential as a tool both to assist in pathologists' diagnoses, and in training

The Artificial Intelligence in Digital Pathology and Digital Radiology: Where Are We?

This book is a reprint of the Special Issue entitled "The Artificial Intelligence in Digital Pathology and Digital Radiology: Where Are We?". Artificial intelligence is extending into the world of both digital radiology and digital pathology, and involves many scholars in the areas of biomedicine, technology, and bioethics. There is a particular need for scholars to focus on both the innovations in this field and the problems hampering integration into a robust and effective process in stable health care models in the health domain. Many professionals involved in these fields of digital health were encouraged to contribute with their experiences. This book contains contributions from various experts across different fields. Aspects of the integration in the health domain have been faced. Particular space was dedicated to overviewing the challenges, opportunities, and problems in both radiology and pathology. Clinal deepens are available in cardiology, the hystopathology of breast cancer, and colonoscopy. Dedicated studies were based on surveys which investigated students and insiders, opinions, attitudes, and self-perception on the integration of artificial intelligence in this field

Computer Vision for Tissue Characterization and Outcome Prediction in Cancer

The aim of this dissertation was to investigate the use of computer vision for tissue characterization and patient outcome prediction in cancer. This work focused on analysis of digitized tissue specimens, which were stained only for basic morphology (i.e. hematoxylin and eosin). The applicability of texture analysis and convolutional neural networks was evaluated for detection of biologically and clinically relevant features. Moreover, novel approaches to guide ground-truth annotation and outcome-supervised learning for prediction of patient survival directly from the tumor tissue images without expert guidance was investigated. We first studied quantification of tumor viability through segmentation of necrotic and viable tissue compartments. We developed a regional texture analysis method, which was trained and tested on whole sections of mouse xenograft models of human lung cancer. Our experiments showed that the proposed segmentation was able to discriminate between viable and non-viable tissue regions with high accuracy when compared to human expert assessment. We next investigated the feasibility of pre-trained convolutional neural networks in analysis of breast cancer tissue, aiming to quantify tumor-infiltrating lymphocytes in the specimens. Interestingly, our results showed that pre-trained convolutional neural networks can be adapted for analysis of histological image data, outperforming texture analysis. The results also indicated that the computerized assessment was on par with pathologist assessments. Moreover, the study presented an image annotation technique guided by specific antibody staining for improved ground-truth labeling. Direct outcome prediction in breast cancer was then studied using a nationwide patient cohort. A computerized pipeline, which incorporated orderless feature aggregation and convolutional image descriptors for outcome-supervised classification, resulted in a risk grouping that was predictive of both disease-specific and overall survival. Surprisingly, further analysis suggested that the computerized risk prediction was also an independent prognostic factor that provided information complementary to the standard clinicopathological factors. This doctoral thesis demonstrated how computer-vision methods can be powerful tools in analysis of cancer tissue samples, highlighting strategies for supervised characterization of tissue entities and an approach for identification of novel prognostic morphological features.Kudosnäytteiden mikroskooppisten piirteiden visuaalinen tarkastelu on yksi tärkeimmistä määrityksistä syöpäpotilaiden diagnosoinnissa ja hoidon suunnittelussa. Edistyneet kuvantamisteknologiat ovat mahdollistaneet histologisten kasvainkudosnäytteiden digitalisoinnin tarkalla resoluutiolla. Näytteiden digitalisoinnin seurauksena niiden analysointiin voidaan soveltaa edistyneitä koneoppimiseen perustuvia konenäön menetelmiä. Tämä väitöskirja tutkii konenäön menetelmien soveltamista syöpäkudosnäytteiden laskennalliseen analyysiin. Työssä tutkitaan yksittäisten histologisten entiteettien, kuten nekroottisen kudoksen ja immuunisolujen automaattista kvantifiointia. Lisäksi työssä esitellään menetelmä potilaan selviytymisen ennustamiseen pelkkään kudosmorfologiaan perustuen

Computational Pathology: A Survey Review and The Way Forward

Computational Pathology CPath is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath is to develop infrastructure and workflows of digital diagnostics as an assistive CAD system for clinical pathology, facilitating transformational changes in the diagnosis and treatment of cancer that are mainly address by CPath tools. With evergrowing developments in deep learning and computer vision algorithms, and the ease of the data flow from digital pathology, currently CPath is witnessing a paradigm shift. Despite the sheer volume of engineering and scientific works being introduced for cancer image analysis, there is still a considerable gap of adopting and integrating these algorithms in clinical practice. This raises a significant question regarding the direction and trends that are undertaken in CPath. In this article we provide a comprehensive review of more than 800 papers to address the challenges faced in problem design all-the-way to the application and implementation viewpoints. We have catalogued each paper into a model-card by examining the key works and challenges faced to layout the current landscape in CPath. We hope this helps the community to locate relevant works and facilitate understanding of the field's future directions. In a nutshell, we oversee the CPath developments in cycle of stages which are required to be cohesively linked together to address the challenges associated with such multidisciplinary science. We overview this cycle from different perspectives of data-centric, model-centric, and application-centric problems. We finally sketch remaining challenges and provide directions for future technical developments and clinical integration of CPath (https://github.com/AtlasAnalyticsLab/CPath_Survey).Comment: Accepted in Elsevier Journal of Pathology Informatics (JPI) 202

Machine Learning Assisted Digital Pathology

Histopatologiset kudosnäytteet sisältävät valtavan määrän tietoa biologisista mekanismeista, jotka vaikuttavat monien tautien ilmenemiseen ja etenemiseen. Tästä syystä histopatologisten näytteiden arviointi on ollut perustana monien tautien diagnostiikassa vuosikymmenien ajan. Perinteinen histopatologinen arviointi on kuitenkin työläs tehtävä ja lisäksi erittäin altis inhimillisille virheille ja voi siten johtaa virheelliseen tai viivästyneeseen diagnoosiin. Viime vuosien teknologinen kehitys on tuonut patologien käyttöön lasiskannerit ja tiedonhallintajarjestelmät ja sitä myötä mahdollistaneet näytelasien digitoinnin ja käynnistäneet koko patologian työnkulun digitalisaation. Histopatologisten näytteiden saatavuus digitaalisina kuvina on puolestaan mahdollistanut älykkäiden algoritmien ja automatisoitujen laskennallisten kuva-analyysityökalujen kehittämisen diagnostiikan tueksi. Koneoppiminen on tekoälyn osa-alue, joka voidaan määritellä datasta oppimiseksi. Kuva-analyysin sovelluksissa, kuvan pikseliarvot muutetaan kvantitatiiviseksi piirre-esitykseksi jonka pohjalta kuva voidaan muuntaa merkitykselliseksi tiedoksi hyvödyntämällä koneoppimista. Vuosien saatossa koneoppimiseen perustuvan kuva-analyysin menetelmät ovat kehittyneet manuaalisesta piirteidenirroituksesta kohti viimevuosien vallitsevia syväoppimiseen pohjautuvia konvoluutioneuroverkkoja. Koneoppimisen hyödyt histopatologisessa arvioinnissa ovat huomattavat, sillä koneoppiminen mahdollistaa kuvien tulkinnan patologiin verrattavalla tarkkuudella ja siten pystyy merkittävästi parantamaan kliinisen patologian diagnostiikan tarkkuutta, toistettavuutta ja tehokkuutta. Tämä väitöstyö esittelee koneoppimiseen pohjautuvia menetelmiä jotka on kehitetty avustamaan kudosnäytteen histopatologista arviointia, vaihetta joka on merkityksellinen niin kliinisessä diagnostiikassa kuin prekliinisissä tutkimuksissa. Työssä esitellään piirteenirroituksen ja koneoppimisen tehokkuus histopatologiseen arviointiin liittyvissä kuva-analyysitehtävissä kuten kudoksen karakterisoinnissa, sekä rintasyövän etäpesäkkeiden, epiteelikudoksen ja tumien tunnistuksessa. Menetelmien lisäksi tässä väitöstyössä on käsitelty keskeisiä haasteita jotka on huomioitava integroitaessa koneoppimismenetelmiä kliiniseen käyttöön. Ennen kaikkea nämä tutkimukset ovat kuitenkin osoittaneet koneoppimisen mahdollisuudet tulevaisuudessa parantaa patologian kliinisten rutiinitehtävien tehokkuutta ja toistettavuutta sekä diagnostiikan laatua.Histopathological tissue samples contain a vast amount of information on underlying biological mechanisms that contribute to disease manifestation and progression. Therefore, diagnosis from histopathological tissue samples has been the gold standard for decades. However, traditional histopathological assessment is a laborious task and prone to human errors, thereby leading to misdiagnosis or delayed diagnosis. The development of whole slide scanners for digitization of tissue glass slides has initiated the transition to a fully digital pathology workflow that allows scanning, interpretation, and management of digital tissue slides. These advances have been the cornerstone for developing intelligent algorithms and automated computational approaches for histopathological assessment and clinical diagnostics. Machine learning is a subcategory of artificial intelligence and can be defined as a process of learning from data. In image analysis tasks, the raw pixel values are transformed into quantitative feature representations. Based on the image data representation, a machine learning model learns a set of rules that can be used to extract meaningful information and knowledge. Over the years, the field of machine learning based image analysis has developed from manually handcrafting complex features to the recent revolution of deep learning and convolutional neural networks. Histopathological assessment can benefit greatly from the ability of machine learning models to discover patterns and connections from the data. Therefore, machine learning holds great promise to improve the accuracy, reproducibility, and efficiency of clinical diagnostics in the field of digital pathology. This thesis is focused on developing machine learning based methods for assisting in the process of histopathological assessment, which is a significant step in clinical diagnostics as well as in preclinical studies. The studies presented in this thesis show the effectiveness of feature engineering and machine learning in histopathological assessment related tasks, such as; tissue characterisation, metastasis detection, epithelial tissue detection, and nuclei detection. Moreover, the studies presented in this thesis address the key challenges related to variation presented in histopathological data as well as the generalisation problem that need to be considered in order to integrate machine learning approaches into clinical practice. Overall, these studies have demonstrated the potential of machine learning for bringing standardization and reproducibility to the process of histopathological assessment