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

Interpretable pap smear cell representation for cervical cancer screening

Screening is critical for prevention and early detection of cervical cancer but it is time-consuming and laborious. Supervised deep convolutional neural networks have been developed to automate pap smear screening and the results are promising. However, the interest in using only normal samples to train deep neural networks has increased owing to class imbalance problems and high-labeling costs that are both prevalent in healthcare. In this study, we introduce a method to learn explainable deep cervical cell representations for pap smear cytology images based on one class classification using variational autoencoders. Findings demonstrate that a score can be calculated for cell abnormality without training models with abnormal samples and localize abnormality to interpret our results with a novel metric based on absolute difference in cross entropy in agglomerative clustering. The best model that discriminates squamous cell carcinoma (SCC) from normals gives 0.908 +- 0.003 area under operating characteristic curve (AUC) and one that discriminates high-grade epithelial lesion (HSIL) 0.920 +- 0.002 AUC. Compared to other clustering methods, our method enhances the V-measure and yields higher homogeneity scores, which more effectively isolate different abnormality regions, aiding in the interpretation of our results. Evaluation using in-house and additional open dataset show that our model can discriminate abnormality without the need of additional training of deep models.Comment: 20 pages, 6 figure

Nuclei & Glands Instance Segmentation in Histology Images: A Narrative Review

Instance segmentation of nuclei and glands in the histology images is an important step in computational pathology workflow for cancer diagnosis, treatment planning and survival analysis. With the advent of modern hardware, the recent availability of large-scale quality public datasets and the community organized grand challenges have seen a surge in automated methods focusing on domain specific challenges, which is pivotal for technology advancements and clinical translation. In this survey, 126 papers illustrating the AI based methods for nuclei and glands instance segmentation published in the last five years (2017-2022) are deeply analyzed, the limitations of current approaches and the open challenges are discussed. Moreover, the potential future research direction is presented and the contribution of state-of-the-art methods is summarized. Further, a generalized summary of publicly available datasets and a detailed insights on the grand challenges illustrating the top performing methods specific to each challenge is also provided. Besides, we intended to give the reader current state of existing research and pointers to the future directions in developing methods that can be used in clinical practice enabling improved diagnosis, grading, prognosis, and treatment planning of cancer. To the best of our knowledge, no previous work has reviewed the instance segmentation in histology images focusing towards this direction.Comment: 60 pages, 14 figure

Cortical Oscillations During a Lateral Balance Perturbation While Walking

The role of sensory systems in the cortical control of dynamic balance was examined using electroencephalography (EEG) recordings during balance perturbations while walking. Specifically, we examined the impact of sensory deficits on cortical oscillations using vibratory stimuli to suppress sensory feedback and by comparing cortical oscillations during balance perturbations while walking in people with sensory deficits associated with cervical myelopathy and neurologically intact controls. Balance during walking provides a rich framework for investigating cortical control using EEG during a functionally relevant task. While this approach is promising, substantial technical challenges remain in recording and processing EEG in the noisy, artifact laden environment associated with walking. We therefore first investigated the role of sensory attenuation in healthy, adult controls within the framework of a simple, motor task. We then examined the effectiveness of using independent component analysis and additional machine learning techniques such as clustering and linear classifiers for differentiating noise from actual brain activity in EEG signals during walking. Finally, we examined a more complicated experimental framework using a custom cable-servomotor system to deliver a lateral pull to the waist of participants with cervical myelopathy while walking and measured their cortical activity using high density EEG. We observed that the attenuation of sensory input in healthy controls induced a similar change in beta band modulation as found previously in spinal cord injury for simple movements of the ankle. During walking, large increases in theta band power throughout the cortex were observed to modulate with lateral balance perturbations. Theta band modulations in the frontal areas of the cortex were significantly delayed in time and displayed a more spatially lateralized cortical localization for participants with cervical myelopathy compared to age-matched, healthy controls. The timing of these theta power modulations were significantly correlated with the initiation of a widening step width correction in response to the balance perturbation. Our results support a link between the modulation of cortical oscillations and sensorimotor integration in simple and complex motor paradigms

DeepHTLV: a Deep Learning Framework for Detecting Human T-Lymphotrophic Virus 1 Integration Sites

In the 1980s, researchers found the first human oncogenic retrovirus called human T-lymphotrophic virus type 1 (HTLV-1). Since then, HTLV-1 has been identified as the causative agent behind several diseases such as adult T-cell leukemia/lymphoma (ATL) and a HTLV-1 associated myelopathy or tropical spastic paraparesis (HAM/TSP). As part of its normal replication cycle, the genome is converted into DNA and integrated into the genome. With several hundreds to thousands of unique viral integration sites (VISs) distributed with indeterminate preference throughout the genome, detection of HTLV-1 VISs is a challenging task. Experimental studies typically use molecular biology techniques such as fluorescent in-situ hybridization (FISH) or using rt-qPCR (reverse transcriptase quantitative PCR) to detect VISs. While these methods are accurate, they cannot be applied in a high throughput manner. Next generation sequencing (NGS) has generated vast amounts of data, resulting in the development of several computational methods for VIS detection such as VERSE, VirusFinder, or DeepVISP for the task of rapid detection VIS across an entire genome. However, no such model exists for predicting HTLV-1 VISs. In this study, we have developed DeepHTLV: the first deep neural network for accurate detection of HTLV-1 insertion sites. We focused on 1) accurately predicting HTLV-1 VISs by extracting and generating superior feature representations and 2) uncovering the cis-regulatory features surrounding the insertion sites. DeepHTLV was implemented as a deep convolutional neural network (CNN) with self-attention architecture after comparing with several other deep neural network structures. To improve model accuracy, we trained the model using a bootstrap balanced sampling method with 10-fold CV. Furthermore, we demonstrated that this model has higher accuracy than several traditional machine learning models, with a modest improvement in area under the curve (AUC) values by 3-10%. To study the cis-regulatory features around HTLV-1 insertion sites, we extracted informative motifs from convolutional layer. Clustering of these motifs yielded eight unique consensus sequence motifs that represented potential integration sites in humans. The informative motif sequences were matched with a known transcription factor (TF) binding profile database, JASPAR2020, with the sequence matching tool TOMTOM. 79 TFs associations were enriched in regions surrounding HTLV-1 VISs. Furthermore, literature screening of HTLV-1, ATL, and HAM/TSP validated nearly half (34) of the predicted TFs interactions. This work demonstrates that DeepHTLV can accurately identify HTLV-1 VISs, elucidate surrounding features regulating these insertion sites, and make biologically meaningful predictions about cis-regulatory elements surrounding the insertion sites

Computational Intelligence in Healthcare

This book is a printed edition of the Special Issue Computational Intelligence in Healthcare that was published in Electronic

Computational Intelligence in Healthcare

The number of patient health data has been estimated to have reached 2314 exabytes by 2020. Traditional data analysis techniques are unsuitable to extract useful information from such a vast quantity of data. Thus, intelligent data analysis methods combining human expertise and computational models for accurate and in-depth data analysis are necessary. The technological revolution and medical advances made by combining vast quantities of available data, cloud computing services, and AI-based solutions can provide expert insight and analysis on a mass scale and at a relatively low cost. Computational intelligence (CI) methods, such as fuzzy models, artificial neural networks, evolutionary algorithms, and probabilistic methods, have recently emerged as promising tools for the development and application of intelligent systems in healthcare practice. CI-based systems can learn from data and evolve according to changes in the environments by taking into account the uncertainty characterizing health data, including omics data, clinical data, sensor, and imaging data. The use of CI in healthcare can improve the processing of such data to develop intelligent solutions for prevention, diagnosis, treatment, and follow-up, as well as for the analysis of administrative processes. The present Special Issue on computational intelligence for healthcare is intended to show the potential and the practical impacts of CI techniques in challenging healthcare applications

The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis

Recently, deep learning frameworks have rapidly become the main methodology for analyzing medical images. Due to their powerful learning ability and advantages in dealing with complex patterns, deep learning algorithms are ideal for image analysis challenges, particularly in the field of digital pathology. The variety of image analysis tasks in the context of deep learning includes classification (e.g., healthy vs. cancerous tissue), detection (e.g., lymphocytes and mitosis counting), and segmentation (e.g., nuclei and glands segmentation). The majority of recent machine learning methods in digital pathology have a pre- and/or post-processing stage which is integrated with a deep neural network. These stages, based on traditional image processing methods, are employed to make the subsequent classification, detection, or segmentation problem easier to solve. Several studies have shown how the integration of pre- and post-processing methods within a deep learning pipeline can further increase the model's performance when compared to the network by itself. The aim of this review is to provide an overview on the types of methods that are used within deep learning frameworks either to optimally prepare the input (pre-processing) or to improve the results of the network output (post-processing), focusing on digital pathology image analysis. Many of the techniques presented here, especially the post-processing methods, are not limited to digital pathology but can be extended to almost any image analysis field