Deep learning interpretability methods for the classification of blood cell images

Durant l’última dècada, el sector mèdic ha adoptat les xarxes neuronals com a eina per ajudar a diagnosticar i comprendre diferents malalties, degut a la seva elevada precisió i versatilitat. No obstant, la seva integració al flux de treball dels patòlegs s'ha vist greument afectada per la naturalesa “Black-Box” que presenten aquests models. Els complexos conceptes matemàtics i estadístics en què es basen aquests models, dificulten enormement la comprensió directa dels criteris de decisió en el qual es basen per fer les seves prediccions. La interpretabilitat de xarxes neuronal té com a objectiu proporcionar explicacions en termes comprensibles a un ésser humà. En aquest projecte, es duu a terme un estudi d’interpretabilitat a la xarxa DisplasiaNet, una xarxa neuronal convolucional especialment optimitzada per classificar les imatges de neutròfils sanguinis perifèrics en Normals o Displastics. Treballant estretament amb patòlegs i amb l’ajut d’una aplicació d’anotacions web construïda a propòsit, s’extreuen les principals característiques morfològiques dels diferents estats cel·lulars. En paral·lel, s’apliquen tècniques d’interpretabilitat d’imatges a la xarxa DisplasiNet, com ara mapes de saliència, mapes d’activació de classes i mapes de sensibilitat envers l’oclusió, per obtenir les caracteristiques que el model considera més rellevants. L'estudi ha descobert que DisplasiaNet detecta displàsia en neutròfils de manera similar als patòlegs, validant així la seva precisió. En primer lloc, es centra en la granularitat del citoplasma i, en segon lloc, en la densitat cromatínica del nucli i la segmentació lobular.Durante la última década, el sector médico ha adoptado ampliamente las redes neuronales como una herramienta para ayudar a diagnosticar y comprender diferentes enfermedades. Sin embargo, su integración en el flujo de trabajo de los patólogos se ha visto gravemente afectado debido a la naturaleza “Black-Box” que presentan estos modelos. Los complejos conceptos matemáticos y estadísticos en los que se basan estos modelos dificultan enormemente la comprensión directa de los criterios decisivos que el modelo emplea para realizar predicciones. La interpretabilidad de redes neuronales tiene como objetivo proporcionar explicaciones en términos comprensibles para un ser humano. En este proyecto, se lleva a cabo un estudio de interpretabilidad de de la red nuronal DisplasiaNet, una red convolucional especialmente optimizada para clasificar imágenes de neutrófilos de sangre periférica en displásicas o normales. Trabajando en estrecha colaboración con patólogos expertos y con la ayuda de una aplicación de anotación web expresamente diseñada, se extraen las principales características morfológicas que presentan los diferentes estados celulares. En paralelo se aplican a DisplasiaNet técnicas de Interpretabilidad de redes neuronales especializadas en el analisis de imágenes tales como Mapas de relevancia, Mapas de activación de clases y Mapas de sensibilidad de oclusión para obtener las características que el modelo considera más relevantes. El estudio ha encontrado que DisplasiaNet detecta displasia en neutrófilos de manera similar a los patólogos expertos, validando así su precisión. En primer lugar, se centra en la granularidad del citoplasma y, en segundo lugar, en la densidad cromatínica del núcleo y la segmentación lobular.During the past decade, the Medical Sector has widely adopted Neural Networks as a tool to help diagnose and to further understand different diseases. This is due to their proven high accuracy and versatility. However, its integration into the pathologists' workflow has been severely affected due to the black box nature these models present. The complex mathematical and statistical concepts these models are based on greatly hinder the direct understanding of the model's decision criteria when these perform predictions. Neural Network Interpretability aims to provide explanations in understandable terms to a human. In this project, a deep learning interpretability study is carried out on DisplasiaNet, a Convolutional Neural Network specially optimized to classify Peripheral Blood Neutrophil images into Dysplastic or Normal. Working closely with expert pathologists and with the help of a purposely built web annotation app, the main morphological characteristics of the different cell states are extracted. Image interpretability techniques such as Saliency Maps, Class Activation Maps, and Occlusion Sensitivity Maps are applied to DisplasiaNet to obtain the features the model considers the most relevant. The study has found that DisplasiaNet detects dysplasia in Neutrophils in a similar manner to expert pathologists, thus validating its accuracy. Firstly it focuses on the granularity of the cytoplasm, and secondly on the nucleus chromatinic density and lobular segmentation

Automated Thalassemia cell image segmentation using hybrid Fuzzy C-Means and K-Means

Thalassemia is a form of hereditary disease. Thalassemia is one of the world's most common illnesses. The morphology of red blood cells is most affected by this disorder. This research proposes a new method of automatically segmenting red blood cells from microscopic blood smear images. The research suggests a novel combination of image processing techniques and extensive preprocessing to achieve superior segmentation performance. In this work, the eleven designated color spaces, with six filters and three contrasts enhancing, Fuzzy c-means and K-means segmentation studied using five evaluation parameters. This evaluation is based on the ground truth image. The Photoshop program performs novel ground truth techniques for multi-object sense (RBC cells). The optimization of all image processing stages was obtained through local image datasets (258 images) obtained from seven thalassemia patients in the Erbil – thalassemia center and five samples of normal blood cells in Children Raparin Teaching Hospital. The image was captured with different light intensities (low, medium, high) and with /without a yellow filter in Biophysics Research lab /Education College / Salahaddin University –Erbil. This study found that the best light intensity for image slide capture utilizing a microscope was medium without using a yellow filter with an accuracy of 0.91± 0.14 and a performance of 95.34%

Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is the most common type of leukemia. The Cancer Genome Atlas Research Network has demonstrated the increasing genomic complexity of acute myeloid leukemia (AML). In addition, the network has facilitated our understanding of the molecular events leading to this deadly form of malignancy for which the prognosis has not improved over past decades. AML is a highly heterogeneous disease, and cytogenetics and molecular analysis of the various chromosome aberrations including deletions, duplications, aneuploidy, balanced reciprocal translocations and fusion of transcription factor genes and tyrosine kinases has led to better understanding and identification of subgroups of AML with different prognoses. Furthermore, molecular classification based on mRNA expression profiling has facilitated identification of novel subclasses and defined high-, poor-risk AML based on specific molecular signatures. However, despite increased understanding of AML genetics, the outcome for AML patients whose number is likely to rise as the population ages, has not changed significantly. Until it does, further investigation of the genomic complexity of the disease and advances in drug development are needed. In this review, leading AML clinicians and research investigators provide an up-to-date understanding of the molecular biology of the disease addressing advances in diagnosis, classification, prognostication and therapeutic strategies that may have significant promise and impact on overall patient survival

Computerised diagnosis of malaria

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Investigating the build-up of precedence effect using reflection masking

The auditory processing level involved in the build‐up of precedence [Freyman et al., J. Acoust. Soc. Am. 90, 874–884 (1991)] has been investigated here by employing reflection masked threshold (RMT) techniques. Given that RMT techniques are generally assumed to address lower levels of the auditory signal processing, such an approach represents a bottom‐up approach to the buildup of precedence. Three conditioner configurations measuring a possible buildup of reflection suppression were compared to the baseline RMT for four reflection delays ranging from 2.5–15 ms. No buildup of reflection suppression was observed for any of the conditioner configurations. Buildup of template (decrease in RMT for two of the conditioners), on the other hand, was found to be delay dependent. For five of six listeners, with reflection delay=2.5 and 15 ms, RMT decreased relative to the baseline. For 5‐ and 10‐ms delay, no change in threshold was observed. It is concluded that the low‐level auditory processing involved in RMT is not sufficient to realize a buildup of reflection suppression. This confirms suggestions that higher level processing is involved in PE buildup. The observed enhancement of reflection detection (RMT) may contribute to active suppression at higher processing levels

Morphological Features of Dysplastic Progression in Epithelium: Quantification of Cytological, Microendoscopic, and Second Harmonic Generation Images

Advances in imaging technology have led to a variety of available clinical and investigational systems. In this collection of studies, we tested the relevance of morphological image feature quantification on several imaging systems and epithelial tissues. Quantification carries the benefit of creating numerical baselines and thresholds of healthy and abnormal tissues, to potentially aid clinicians in determining a diagnosis, as well as providing researchers with standardized, unbiased results for future dissemination and comparison. Morphological image features in proflavine stained oral cells were compared qualitatively to traditional Giemsa stained cells, and then we quantified the nuclear to cytoplasm ratio. We determined that quantification of proflavine stained cells matched our hypothesis, as the nuclei in oral carcinoma cells were significantly larger than healthy oral cells. Proflavine has been used in conjunction with translational fluorescence microendoscopy of the gastrointestinal tract, and we demonstrated the ability of our custom algorithm to accurately (up to 85% sensitivity) extract colorectal crypt area and circularity data, which could minimize the burden of training on clinicians. In addition, we proposed fluorescein as an alternative fluorescent dye, providing comparable crypt area and circularity information. In order to investigate the morphological changes of crypts via the supporting collagen structures, we adapted our quantification algorithm to analyze crypt area, circularity, and an additional shape parameter in second harmonic generation images of label-free freshly resected murine epithelium. Murine models of colorectal cancer (CRC) were imaged at early and late stages of tumor progression, and we noted significant differences between the Control groups and the late cancer stages, with some differences between early and late stages of CRC progression

Automatic detection and classification of leukaemia cells

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Today, there is a substantial number of software and research groups that focus on the development of image processing software to extract useful information from medical images, in order to assist and improve patient diagnosis. The work presented in this thesis is centred on processing of images of blood and bone marrow smears of patients suffering from leukaemia, a common type of cancer. In general, cancer is due to aberrant gene expression, which is caused by either mutations or epigenetic changes in DNA. Poor diet and unhealthy lifestyle may trigger or contribute to these changes, although the underlying mechanism is often unknown. Importantly, many cancer types including leukaemia are curable and patient survival and treatment can be improved, subject to prompt diagnosis. In particular, this study focuses on Acute Myeloid Leukaemia (AML), which can be of eight distinct types (M0 to M7), with the main objective to develop a methodology to automatically detect and classify leukaemia cells into one of the above types. The data was collected from the Department of Haematology, Universiti Sains Malaysia, in Malaysia. Three main methods, namely Cellular Automata, Heuristic Search and classification using Neural Networks are facilitated. In the case of Cellular Automata, an improved method based on the 8-neighbourhood and rules were developed to remove noise from images and estimate the radius of the potential blast cells contained in them. The proposed methodology selects the starting points, corresponding to potential blast cells, for the subsequent seeded heuristic search. The Seeded Heuristic employs a new fitness function for blast cell detection. Furthermore, the WEKA software is utilised for classification of blast cells and hence images, into AML subtypes. As a result accuracy of 97.22% was achieved in the classification of blasts into M3 and other AML subtypes. Finally, these algorithms are integrated into an automated system for image processing. In brief, the research presented in this thesis involves the use of advanced computational techniques for processing and classification of medical images, that is, images of blood samples from patients suffering from leukaemia.The Institute of Higher Education of Malaysia and the Universiti Sains Islam Malaysia (USIM)

Host-parasite interactions in Galapagos seabirds

Parasites exhibit a wide range of life history strategies that contribute to different dispersal abilities, host specialization, transmission modes, life-cycle complexity and population structure. Understanding dispersal rates in hosts and parasites is instrumental in defining the scale at which coevolution may be occurring. In order to better understand how and when parasites move between different hosts, I studied a seabird – Hippoboscid fly ectoparasite (and vector) – Haemosporidian parasite system in the Galapagos Islands. I began by describing the Haemosporidian parasites of Galapagos seabirds, discovering a Plasmodium species parasite in Galapagos Penguins (Sphensicus mendiculus), and a new clade of Hippoboscid-vectored parasites belonging to the subgenus Haemoproteus infecting frigatebirds (Fregata spp.) and gulls (Creagrus furcatus). Despite strong genetic differentiation between Galapagos frigatebirds and their conspecifics, we found no genetic differentiation in their Haemoproteus parasite. This led me hypothesize that the movement of the Haemosporidian parasite was facilitated by the movement of the Hippoboscid fly vector. In order to answer this question, I used a comparative population genetic study of Galapagos Great Frigatebirds (F. minor), Nazca Boobies (Sula granti), and their respective Hippoboscid fly parasites (Olfersia spinifera, O. aenescens) to better understand movement of flies at the geographic scale of the archipelago. I found high levels of gene flow in both fly species, despite marked differences in the degree of population genetic structure of their bird hosts. This suggests that host movement, (and therefore parasite movement), is not necessarily associated with true host dispersal, where dispersal is followed by successful reproduction. Finally, I examined local (within island colony) transmission in the Great Frigatebird, Haemoproteus iwa, Olfersia spinifera system. I inferred movement, or host-switching, by analyzing host (frigatebird) microsatellite markers run on DNA amplified from the fly. Using the most variable microsatellite markers, we are able to identify host genotypes in bloodmeals that do not match the host from which the fly was collected. Flies that were not infected with H. iwa were more likely to have a bloodmeal that did not match the genotype of their host and female birds were the more likely recipients of host-switching flies