Deep Learning for Multiclass Classification, Predictive Modeling and Segmentation of Disease Prone Regions in Alzheimer’s Disease

One of the challenges facing accurate diagnosis and prognosis of Alzheimer’s Disease (AD) is identifying the subtle changes that define the early onset of the disease. This dissertation investigates three of the main challenges confronted when such subtle changes are to be identified in the most meaningful way. These are (1) the missing data challenge, (2) longitudinal modeling of disease progression, and (3) the segmentation and volumetric calculation of disease-prone brain areas in medical images. The scarcity of sufficient data compounded by the missing data challenge in many longitudinal samples exacerbates the problem as we seek statistical meaningfulness in multiclass classification and regression analysis. Although there are many participants in the AD Neuroimaging Initiative (ADNI) study, many of the observations have a lot of missing features which often lead to the exclusion of potentially valuable data points that could add significant meaning in many ongoing experiments. Motivated by the necessity of examining all participants, even those with missing tests or imaging modalities, multiple techniques of handling missing data in this domain have been explored. Specific attention was drawn to the Gradient Boosting (GB) algorithm which has an inherent capability of addressing missing values. Prior to applying state-of-the-art classifiers such as Support Vector Machine (SVM) and Random Forest (RF), the impact of imputing data in common datasets with numerical techniques has been also investigated and compared with the GB algorithm. Furthermore, to discriminate AD subjects from healthy control individuals, and Mild Cognitive Impairment (MCI), longitudinal multimodal heterogeneous data was modeled using recurring neural networks (RNNs). In the segmentation and volumetric calculation challenge, this dissertation places its focus on one of the most relevant disease-prone areas in many neurological and neurodegenerative diseases, the hippocampus region. Changes in hippocampus shape and volume are considered significant biomarkers for AD diagnosis and prognosis. Thus, a two-stage model based on integrating the Vision Transformer and Convolutional Neural Network (CNN) is developed to automatically locate, segment, and estimate the hippocampus volume from the brain 3D MRI. The proposed architecture was trained and tested on a dataset containing 195 brain MRIs from the 2019 Medical Segmentation Decathlon Challenge against the manually segmented regions provided therein and was deployed on 326 MRI from our own data collected through Mount Sinai Medical Center as part of the 1Florida Alzheimer Disease Research Center (ADRC)

Physical energies to the rescue of damaged tissues

open10siRhythmic oscillatory patterns sustain cellular dynamics, driving the concerted action of regulatory molecules, microtubules, and molecular motors. We describe cellular microtubules as oscillators capable of synchronization and swarming, generating mechanical and electric patterns that impact biomolecular recognition. We consider the biological relevance of seeing the inside of cells populated by a network of molecules that behave as bioelectronic circuits and chromophores. We discuss the novel perspectives disclosed by mechanobiology, bioelectromagnetism, and photobiomodulation, both in term of fundamental basic science and in light of the biomedical implication of using physical energies to govern (stem) cell fate. We focus on the feasibility of exploiting atomic force microscopy and hyperspectral imaging to detect signatures of nanomotions and electromagnetic radiation (light), respectively, generated by the stem cells across the specification of their multilineage repertoire. The chance is reported of using these signatures and the diffusive features of physical waves to direct specifically the differentiation program of stem cells in situ, where they already are resident in all the tissues of the human body. We discuss how this strategy may pave the way to a regenerative and precision medicine without the needs for (stem) cell or tissue transplantation. We describe a novel paradigm based upon boosting our inherent ability for self-healing.openFacchin F.; Canaider S.; Tassinari R.; Zannini C.; Bianconi E.; Taglioli V.; Olivi E.; Cavallini C.; Tausel M.; Ventura C.Facchin F.; Canaider S.; Tassinari R.; Zannini C.; Bianconi E.; Taglioli V.; Olivi E.; Cavallini C.; Tausel M.; Ventura C

When the machine does not know measuring uncertainty in deep learning models of medical images

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonRecently, Deep learning (DL), which involves powerful black box predictors, has outperformed human experts in several medical diagnostic problems. However, these methods focus exclusively on improving the accuracy of point predictions without assessing their outputs’ quality and ignore the asymmetric cost involved in different types of misclassification errors. Neural networks also do not deliver confidence in predictions and suffer from over and under confidence, i.e. are not well calibrated. Knowing how much confidence there is in a prediction is essential for gaining clinicians’ trust in the technology. Calibrated uncertainty quantification is a challenging problem as no ground truth is available. To address this, we make two observations: (i) cost-sensitive deep neural networks with Dropweights models better quantify calibrated predictive uncertainty, and (ii) estimated uncertainty with point predictions in Deep Ensembles Bayesian Neural Networks with DropWeights can lead to a more informed decision and improve prediction quality. This dissertation focuses on quantifying uncertainty using concepts from cost-sensitive neural networks, calibration of confidence, and Dropweights ensemble method. First, we show how to improve predictive uncertainty by deep ensembles of neural networks with Dropweights learning an approximate distribution over its weights in medical image segmentation and its application in active learning. Second, we use the Jackknife resampling technique to correct bias in quantified uncertainty in image classification and propose metrics to measure uncertainty performance. The third part of the thesis is motivated by the discrepancy between the model predictive error and the objective in quantified uncertainty when costs for misclassification errors or unbalanced datasets are asymmetric. We develop cost-sensitive modifications of the neural networks in disease detection and propose metrics to measure the quality of quantified uncertainty. Finally, we leverage an adaptive binning strategy to measure uncertainty calibration error that directly corresponds to estimated uncertainty performance and address problematic evaluation methods. We evaluate the effectiveness of the tools on nuclei images segmentation, multi-class Brain MRI image classification, multi-level cell type-specific protein expression prediction in ImmunoHistoChemistry (IHC) images and cost-sensitive classification for Covid-19 detection from X-Rays and CT image dataset. Our approach is thoroughly validated by measuring the quality of uncertainty. It produces an equally good or better result and paves the way for the future that addresses the practical problems at the intersection of deep learning and Bayesian decision theory. In conclusion, our study highlights the opportunities and challenges of the application of estimated uncertainty in deep learning models of medical images, representing the confidence of the model’s prediction, and the uncertainty quality metrics show a significant improvement when using Deep Ensembles Bayesian Neural Networks with DropWeights

Transfer entropy—a model-free measure of effective connectivity for the neurosciences

Understanding causal relationships, or effective connectivity, between parts of the brain is of utmost importance because a large part of the brain’s activity is thought to be internally generated and, hence, quantifying stimulus response relationships alone does not fully describe brain dynamics. Past efforts to determine effective connectivity mostly relied on model based approaches such as Granger causality or dynamic causal modeling. Transfer entropy (TE) is an alternative measure of effective connectivity based on information theory. TE does not require a model of the interaction and is inherently non-linear. We investigated the applicability of TE as a metric in a test for effective connectivity to electrophysiological data based on simulations and magnetoencephalography (MEG) recordings in a simple motor task. In particular, we demonstrate that TE improved the detectability of effective connectivity for non-linear interactions, and for sensor level MEG signals where linear methods are hampered by signal-cross-talk due to volume conduction

Deep MR to CT Synthesis for PET/MR Attenuation Correction

Positron Emission Tomography - Magnetic Resonance (PET/MR) imaging combines the functional information from PET with the flexibility of MR imaging. It is essential, however, to correct for photon attenuation when reconstructing PETs, which is challenging for PET/MR as neither modality directly image tissue attenuation properties. Classical MR-based computed tomography (CT) synthesis methods, such as multi-atlas propagation, have been the method of choice for PET attenuation correction (AC), however, these methods are slow and suffer from the poor ability to handle anatomical abnormalities. To overcome this limitation, this thesis explores the rising field of artificial intelligence in order to develop novel methods for PET/MR AC. Deep learning-based synthesis methods such as the standard U-Net architecture are not very stable, accurate, and robust to small variations in image appearance. Thus, the first proposed MR to CT synthesis method deploys a boosting strategy, where multiple weak predictors build a strong predictor providing a significant improvement in CT and PET reconstruction accuracy. Standard deep learning-based methods as well as more advanced methods like the first proposed method show issues in the presence of very complex imaging environments and large images such as whole-body images. The second proposed method learns the image context between whole-body MRs and CTs through multiple resolutions while simultaneously modelling uncertainty. Lastly, as the purpose of synthesizing a CT is to better reconstruct PET data, the use of CT-based loss functions is questioned within this thesis. Such losses fail to recognize the main objective of MR-based AC, which is to generate a synthetic CT that, when used for PET AC, makes the reconstructed PET as close as possible to the gold standard PET. The third proposed method introduces a novel PET-based loss that minimizes CT residuals with respect to the PET reconstruction

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task