Automatic detection of pathological regions in medical images

Medical images are an essential tool in the daily clinical routine for the detection, diagnosis, and monitoring of diseases. Different imaging modalities such as magnetic resonance (MR) or X-ray imaging are used to visualize the manifestations of various diseases, providing physicians with valuable information. However, analyzing every single image by human experts is a tedious and laborious task. Deep learning methods have shown great potential to support this process, but many images are needed to train reliable neural networks. Besides the accuracy of the final method, the interpretability of the results is crucial for a deep learning method to be established. A fundamental problem in the medical field is the availability of sufficiently large datasets due to the variability of different imaging techniques and their configurations. The aim of this thesis is the development of deep learning methods for the automatic identification of anomalous regions in medical images. Each method is tailored to the amount and type of available data. In the first step, we present a fully supervised segmentation method based on denoising diffusion models. This requires a large dataset with pixel-wise manual annotations of the pathological regions. Due to the implicit ensemble characteristic, our method provides uncertainty maps to allow interpretability of the model’s decisions. Manual pixel-wise annotations face the problems that they are prone to human bias, hard to obtain, and often even unavailable. Weakly supervised methods avoid these issues by only relying on image-level annotations. We present two different approaches based on generative models to generate pixel-wise anomaly maps using only image-level annotations, i.e., a generative adversarial network and a denoising diffusion model. Both perform image-to-image translation between a set of healthy and a set of diseased subjects. Pixel-wise anomaly maps can be obtained by computing the difference between the original image of the diseased subject and the synthetic image of its healthy representation. In an extension of the diffusion-based anomaly detection method, we present a flexible framework to solve various image-to-image translation tasks. With this method, we managed to change the size of tumors in MR images, and we were able to add realistic pathologies to images of healthy subjects. Finally, we focus on a problem frequently occurring when working with MR images: If not enough data from one MR scanner are available, data from other scanners need to be considered. This multi-scanner setting introduces a bias between the datasets of different scanners, limiting the performance of deep learning models. We present a regularization strategy on the model’s latent space to overcome the problems raised by this multi-site setting

Neural connectivity in syntactic movement processing

Linguistic theory suggests non-canonical sentences subvert the dominant agent-verb-theme order in English via displacement of sentence constituents to argument (NP-movement) or non-argument positions (wh-movement). Both processes have been associated with the left inferior frontal gyrus and posterior superior temporal gyrus, but differences in neural activity and connectivity between movement types have not been investigated. In the current study, functional magnetic resonance imaging data were acquired from 21 adult participants during an auditory sentence-picture verification task using passive and active sentences contrasted to isolate NP-movement, and object- and subject-cleft sentences contrasted to isolate wh-movement. Then, functional magnetic resonance imaging data from regions common to both movement types were entered into a dynamic causal modeling analysis to examine effective connectivity for wh-movement and NP-movement. Results showed greater left inferior frontal gyrus activation for Wh > NP-movement, but no activation for NP > Wh-movement. Both types of movement elicited activity in the opercular part of the left inferior frontal gyrus, left posterior superior temporal gyrus, and left medial superior frontal gyrus. The dynamic causal modeling analyses indicated that neither movement type significantly modulated the connection from the left inferior frontal gyrus to the left posterior superior temporal gyrus, nor vice-versa, suggesting no connectivity differences between wh- and NP-movement. These findings support the idea that increased complexity of wh-structures, compared to sentences with NP-movement, requires greater engagement of cognitive resources via increased neural activity in the left inferior frontal gyrus, but both movement types engage similar neural networks.This work was supported by the NIH-NIDCD, Clinical Research Center Grant, P50DC012283 (PI: CT), and the Graduate Research Grant and School of Communication Graduate Ignition Grant from Northwestern University (awarded to EE). (P50DC012283 - NIH-NIDCD, Clinical Research Center Grant; Graduate Research Grant and School of Communication Graduate Ignition Grant from Northwestern University)Published versio

Anomaly Detection in Natural Scene Images Based on Enhanced Fine-Grained Saliency and Fuzzy Logic

This paper proposes a simple yet effective method for anomaly detection in natural scene images improving natural scene text detection and recognition. In the last decade, there has been significant progress towards text detection and recognition in natural scene images. However, in cases where there are logos, company symbols, or other decorative elements for text, existing methods do not perform well. This work considers such misclassified components, which are part of the text as anomalies, and presents a new idea for detecting such anomalies in the text for improving text detection and recognition in natural scene images. The proposed method considers the result of the existing text detection method as input for segmenting characters or components based on saliency map and rough set theory. For each segmented component, the proposed method extracts feature from the saliency map based on density, pixel distribution, and phase congruency to classify text and non-text components by exploring a fuzzy-based classifier. To verify the effectiveness of the method, we have performed experiments on several benchmark datasets of natural scene text detection, namely, MSRATD-500 and SVT. Experimental results show the efficacy of the proposed method over the existing ones for text detection and recognition in these datasets

Deep generative modelling of the imaged human brain

Human-machine symbiosis is a very promising opportunity for the field of neurology given that the interpretation of the imaged human brain is a trivial feat for neither entity. However, before machine learning systems can be used in real world clinical situations, many issues with automated analysis must first be solved. In this thesis I aim to address what I consider the three biggest hurdles to the adoption of automated machine learning interpretative systems. For each issue, I will first elucidate the reader on its importance given the overarching narratives of both neurology and machine learning, and then showcase my proposed solutions to these issues through the use of deep generative models of the imaged human brain. First, I start by addressing what is an uncontroversial and universal sign of intelligence: the ability to extrapolate knowledge to unseen cases. Human neuroradiologists have studied the anatomy of the healthy brain and can therefore, with some success, identify most pathologies present on an imaged brain, even without having ever been previously exposed to them. Current discriminative machine learning systems require vast amounts of labelled data in order to accurately identify diseases. In this first part I provide a generative framework that permits machine learning models to more efficiently leverage unlabelled data for better diagnoses with either none or small amounts of labels. Secondly, I address a major ethical concern in medicine: equitable evaluation of all patients, regardless of demographics or other identifying characteristics. This is, unfortunately, something that even human practitioners fail at, making the matter ever more pressing: unaddressed biases in data will become biases in the models. To address this concern I suggest a framework through which a generative model synthesises demographically counterfactual brain imaging to successfully reduce the proliferation of demographic biases in discriminative models. Finally, I tackle the challenge of spatial anatomical inference, a task at the centre of the field of lesion-deficit mapping, which given brain lesions and associated cognitive deficits attempts to discover the true functional anatomy of the brain. I provide a new Bayesian generative framework and implementation that allows for greatly improved results on this challenge, hopefully, paving part of the road towards a greater and more complete understanding of the human brain

Alzheimer’s And Parkinson’s Disease Classification Using Deep Learning Based On MRI: A Review

Neurodegenerative disorders present a current challenge for accurate diagnosis and for providing precise prognostic information. Alzheimer’s disease (AD) and Parkinson's disease (PD), may take several years to obtain a definitive diagnosis. Due to the increased aging population in developed countries, neurodegenerative diseases such as AD and PD have become more prevalent and thus new technologies and more accurate tests are needed to improve and accelerate the diagnostic procedure in the early stages of these diseases. Deep learning has shown significant promise in computer-assisted AD and PD diagnosis based on MRI with the widespread use of artificial intelligence in the medical domain. This article analyses and evaluates the effectiveness of existing Deep learning (DL)-based approaches to identify neurological illnesses using MRI data obtained using various modalities, including functional and structural MRI. Several current research issues are identified toward the conclusion, along with several potential future study directions

The Applications of Discrete Wavelet Transform in Image Processing: A Review

This paper reviews the newly published works on applying waves to image processing depending on the analysis of multiple solutions. the wavelet transformation reviewed in detail including wavelet function, integrated wavelet transformation, discrete wavelet transformation, rapid wavelet transformation, DWT properties, and DWT advantages. After reviewing the basics of wavelet transformation theory, various applications of wavelet are reviewed and multi-solution analysis, including image compression, image reduction, image optimization, and image watermark. In addition, we present the concept and theory of quadruple waves for the future progress of wavelet transform applications and quadruple solubility applications. The aim of this paper is to provide a wide-ranging review of the survey found able on wavelet-based image processing applications approaches. It will be beneficial for scholars to execute effective image processing applications approaches

Low spatial resolution thermal monitoring of volcanoes from space

Low spatial resolution thermal data from the Advanced Very High Resolution Radiometer (AVHRR) have been available for any cloud-free sub-aerial volcano at least 4 times a day since 1979. Although extensively used to monitor ash plumes injected into the atmosphere by volcanic eruptions, this cheap, directly and regularly available data source has never been thoroughly examined to determine its utility for real-time monitoring of high temperature volcanic activity occurring at the surface. This thesis is therefore concerned with developing techniques to locate, measure and monitor high temperature volcanic phenomena in AVHRR data.To locate high temperature phenomena, I develop an automated thresholding algorithm which roams an image flagging thermal anomalies. The major advantage of this technique over existing thresholding techniques developed for fires is that it requires no manual input. By running the algorithm on 90 images acquired during the 1991 to 1993 effusive eruption at Mount Etna, pixels containing active lava or high temperature open vents were located, with the program completing within 1 second. This permitted lava flow location to within ±1 km of known topographic features, roads and towns. The flexibility of the thresholding algorithm to detect other high temperature sources, over a variety of regions and seasons, was demonstrated by applying the algorithm to images containing wild-fires burning along the east coast of Australia during January 1994.For quantitative analysis, I develop a new approach capable of estimating the size and temperature of at least three sub-pixel thermal components. This approach enables lava flow area and thermal flux to be constrained more accurately than is possible using established techniques which allow measurement of just two thermal components.Using the area and thermal flux results, I refine an algorithm to estimate lava effusion rates. This in turn allows calculation of lava flow field cumulative and total volumes. Application to data for effusive activity at Etna during 1985 and between 1991 and 1993 gives mean effusion rates of 3 s-1 during both eruptions, building flow fields of 16-29 x 106 and 220-300 x 106 m3 respectively. Both volume estimates are in good agreement with ground-based estimates of 19 x 106 and 235 x 106 m3 respectively. In both cases, time series of area, thermal flux, effusion rate and cumulative volume data reveal phases of waxing and waning eruptive activity, in close agreement with those observed from the ground.I also present simple approaches which use spectral radiance from the volcanic source to monitor volcanic activity through time and to distinguish between different types of volcanic phenomena. At Stromboli, troughs and peaks in a spectral radiance time-series respectively correlate with periods "normal" explosive activity and less frequent effusive events. A scatter plot of spectral radiances from the volcanic source acquired in two thermal wavebands, one in the mid-infrared and one in the thermal infrared, allows lava lakes, flows and open vents to be distinguished according to their position in the scatter.I use a number of case studies, drawn from effusive activity at Etna, Krafla, Togo, Cerro Negro and Erebus, as well as pyroclastic and degassing activity at Lascar, Vulcano, Etna and Stromboli, to show how AVHRR can be used to measure and monitor high temperature volcanic phenomena. If data analysis were carried out on reception, regular location and monitoring of on-going effusive eruptions, as well as persistent high temperature phenomena, would be possible