Improving Mobile-Based Cervical Cytology Screening: A Deep Learning Nucleus-Based Approach for Lesion Detection

Liquid-based cytology (LBC) plays a crucial role in the effective early detection of cervical cancer, contributing to substantially decreasing mortality rates. However, the visual examination of microscopic slides is a challenging, time-consuming, and ambiguous task. Shortages of specialized staff and equipment are increasing the interest in developing artificial intelligence (AI)-powered portable solutions to support screening programs. This paper presents a novel approach based on a RetinaNet model with a ResNet50 backbone to detect the nuclei of cervical lesions on mobile-acquired microscopic images of cytology samples, stratifying the lesions according to The Bethesda System (TBS) guidelines. This work was supported by a new dataset of images from LBC samples digitalized with a portable smartphone-based microscope, encompassing nucleus annotations of 31,698 normal squamous cells and 1395 lesions. Several experiments were conducted to optimize the model’s detection performance, namely hyperparameter tuning, transfer learning, detected class adjustments, and per-class score threshold optimization. The proposed nucleus-based methodology improved the best baseline reported in the literature for detecting cervical lesions on microscopic images exclusively acquired with mobile devices coupled to the μSmartScope prototype, with per-class average precision, recall, and F1 scores up to 17.6%, 22.9%, and 16.0%, respectively. Performance improvements were obtained by transferring knowledge from networks pre-trained on a smaller dataset closer to the target application domain, as well as including normal squamous nuclei as a class detected by the model. Per-class tuning of the score threshold also allowed us to obtain a model more suitable to support screening procedures, achieving F1 score improvements in most TBS classes. While further improvements are still required to use the proposed approach in a clinical context, this work reinforces the potential of using AI-powered mobile-based solutions to support cervical cancer screening. Such solutions can significantly impact screening programs worldwide, particularly in areas with limited access and restricted healthcare resources.info:eu-repo/semantics/publishedVersio

Mobile-Based Analysis of Malaria-Infected Thin Blood Smears: Automated Species and Life Cycle Stage Determination

Microscopy examination has been the pillar of malaria diagnosis, being the recommended procedure when its quality can be maintained. However, the need for trained personnel and adequate equipment limits its availability and accessibility in malaria-endemic areas. Rapid, accurate, accessible diagnostic tools are increasingly required, as malaria control programs extend parasite-based diagnosis and the prevalence decreases. This paper presents an image processing and analysis methodology using supervised classification to assess the presence of malaria parasites and determine the species and life cycle stage in Giemsa-stained thin blood smears. The main differentiation factor is the usage of microscopic images exclusively acquired with low cost and accessible tools such as smartphones, a dataset of 566 images manually annotated by an experienced parasilogist being used. Eight different species-stage combinations were considered in this work, with an automatic detection performance ranging from 73.9% to 96.2% in terms of sensitivity and from 92.6% to 99.3% in terms of specificity. These promising results attest to the potential of using this approach as a valid alternative to conventional microscopy examination, with comparable detection performances and acceptable computational times.Financial support from North Portugal Regional Operational Programme (NORTE2020), Portugal 2020 and the European Regional Development Fund (ERDF) from the European Union through the project Deus ex Machina: Symbiotic Technology for Societal Efficiency Gains’, NORTE-01-0145-FEDER-000026.N/

Micropositioning tracking system

Tato bakalářská práce se zabývá návrhem a konstrukcí mikroposuvného zařízení, které bude za pomoci kamery sledovat pohyb robotické myši ve vymezeném prostoru. Hlavním úkolem bude sestavit pohyblivou platformu, jejímž cílem bude nacházet se v neustálém kontaktu s robotem pod ní a kopírovat jeho 2D trajektorii. Zařízení bude v budoucnu aplikováno na živou myš pro in vivo experimenty holografické endoskopie.This bachelor's thesis deals with the design and construction of a micro positioning device that will monitor the movement of a robotic mouse in a defined space with the help of a camera. The main task will be to assemble a moving platform, the aim of which will be to be in constant contact with the robot below it and to copy its 2D trajectory. The device will then be applied to a live mouse for in vivo holographic endoscopy experiments.

Combining machine learning and deep learning approaches to detect cervical cancer in cytology images

This dissertation is centred around the implementation and optimization of a hybrid pipeline for the identification and stratification of abnormal cell regions in cytology images, combining state of the art deep learning (DL) approaches and conventional machine learning (ML) models.Cervical cancer is the fourth most common cancer in women. When diagnosed early on, it is one of the most successfully treatable types of cancer. As such, screening tests are very effective as a prevention measure. These tests involve the analysis of microscopic fields of cytology samples which, when performed manually, is a very demanding task, requiring highly specialized laboratory technologists (cytotechs). Due to this, there has been a great interest in automating the overall screening process. Most of these computer-aided diagnosis systems subject the images from each sample to a set of steps, more notably focus and adequacy assessment, region of interest identification and respective classification. This work is focused on the last two stages, more specifically, the detection of abnormal regions and the classification of their abnormality level. The main approaches can be divided into two types: deep learning architectures and conventional machine learning models, both presenting their own set of advantages and disadvantages. This work explores the combination of both of these approaches in hybrid pipelines to minimize the problems of each one whilst taking advantage of the best they have to offer, ultimately contributing to a decision support system for cervical cancer diagnosis. More specifically, it is proposed a deep-learning approach for the detection of the regions of interest and respective bounding-box generation, followed by a simpler machine-learning model for their classification. Furthermore, a comparative analysis of different hybrid pipelines and algorithms will also be performed, aiming to support future research of similar solutions

Statistical Comparison of Different Machine-Learning Approaches for Malaria Parasites Detection in Microscopic Images

A malária é um grave problema de saúde pública em todo o mundo, particularmente nos países em desenvolvimento (cerca de 80% dos casos ocorrem em África), pondo em risco a vida de milhões de pessoas.Causada por 4 espécies diferentes de parasitas, cada um com diferentes estágios de evolução, a aproximação do diagnóstico correto sem acesso a equipamentos caros é complexo. Nos últimos anos, a investigação tem-se centrado na aceleração e redução dos custos deste diagnóstico, recorrendo à classificação de imagens microscópicas, com base em processos de Machine Learning.Ainda assim, persiste na maioria das abordagens a ausência constante de comparação estatística sistemática na literatura que suporta uma técnica ou recurso particular.Assim, os objetivos desta dissertação são: (i) projetar e executar uma comparação estatística de diferentes abordagens de ML para a detecção de tais parasitas em imagens microscópicas, (ii) identificar quais características são mais relevantes para a previsão, (iii) identificar quais modelos E as técnicas obtêm os melhores resultados, equilibrando a precisão e o recall, e (iv) investigando a aplicabilidade das técnicas de aprendizagem profunda e de conjunto.A conclusão bem-sucedida desta dissertação capacitará os países em desenvolvimento com ferramentas de diagnóstico mais rápidas, mais baratas e mais precisas, melhorando diretamente a vida e a saúde de populações.Malaria is a severe public health problem across the world, particularly in developing countries (≈80% of the cases occur in Africa), putting at special risk the most unprotected groups of society: children and pregnant women. Since it can be caused by 4 different species of parasites, each having different stages of evolution, approaching the right diagnosis without access to costly equipment is complex. Ergo, research has focused on speeding up and lowering the costs of its diagnosis, by resorting to automatic machine classification of microscopic images. Still, most approaches rely on simplistic, single-model classifiers, with a constant absence of a systematic statistical comparison in the literature that supports a particular technique or feature.Hence, this dissertation presents: (i) design and execute a statistical comparison of different ML approaches for the detection of such parasites in microscopic images, (ii) identify which features are more relevant for prediction, and (iii) identify which models and techniques achieve the best results, balancing precision and recall.Given the stated problem, and before approaching it, it was performed an initial statistical analysis to the dataset, to discover its proportions and to detect highly correlated features.After knowing the data, it was developed a framework that (i) optimizes the values of the considered classification and feature selection algorithms, (ii) computes a statistical comparison of different machine learning approaches to the same dataset, using different metrics on the cross validation, where there were used different metrics to measure the performance value variation and evaluate which one is consistent with the data, and, finally, (iii) performs a statistical hypothesis test,to guarantee that the data model with the best performance is distinct from all the others considered in this study. As result, one can verify an improvement over the established baseline, by using a Fdr feature selection method followed by a Ada Boosting classifier with 350 estimators