Alfiia Galimzianova

Stanford University | SU

In recent years, large strides have been taken in developing machine learning methods for dermatological applications, supported in part by the success of deep learning (DL). To date, diagnosing diseases from images is one of the most explored applications of DL within dermatology. Convolutional neural networks (ConvNets) are the most common (DL) m...

Purpose: To develop a deep learning-based risk stratification system for thyroid nodules using US cine images. Materials and methods: In this retrospective study, 192 biopsy-confirmed thyroid nodules (175 benign, 17 malignant) in 167 unique patients (mean age, 56 years ± 16 [SD], 137 women) undergoing cine US between April 2017 and May 2018 with...

OBJECTIVE. The purpose of this study was to explore whether a quantitative framework can be used to sonographically differentiate benign and malignant thyroid nodules at a level comparable to that of experts. MATERIALS AND METHODS. A dataset of ultrasound images of 92 biopsy-confirmed nodules was collected retrospectively. The nodules were delineat...

Multiple sclerosis (MS) is a disease characterized by demyelinating lesions in the brain and spinal cord. Quantification of the amount of change in MS lesions in magnetic resonance imaging (MRI) over time is important for evaluation of drug effectiveness in clinical trials. Manual analysis of such longitudinal datasets is time- and cost prohibitive...

We propose a computational framework for automated cancer risk estimation of thyroid nodules visualized in ultrasound (US) images. Our framework estimates the probability of nodule malignancy using random forests on a rich set of computational features. An expert radiologist annotated thyroid nodules in 93 biopsy-confirmed patients using semantic i...

Quantified volume and count of white-matter lesions based on magnetic resonance (MR) images are important biomarkers in several neurodegenerative diseases. For a routine extraction of these biomarkers an accurate and reliable automated lesion segmentation is required. To objectively and reliably determine a standard automated method, however, creat...

Multiple sclerosis (MS) is a neurological disease characterized by focal lesions and morphological changes in the brain captured on magnetic resonance (MR) images. However, extraction of the corresponding imaging markers requires accurate segmentation of normal-appearing brain structures (NABS) and the lesions in MR images. On MR images of healthy...

Quantitative analysis of brain MRI is routine for many neurological diseases and conditions and relies on accurate segmentation of structures of interest. Deep learning-based segmentation approaches for brain MRI are gaining interest due to their self-learning and generalization ability over large amounts of data. As the deep learning architectures...

Characterization of carotid plaque composition, more specifically the amount of lipid core, fibrous tissue, and calcified tissue, is an important task for the identification of plaques that are prone to rupture, and thus for early risk estima-tion of cardiovascular and cerebrovascular events. Due to its low costs and wide availability, carotid ultr...

White-matter lesions are associated to several diseases, which can be characterized by neuroimaging biomarkers through lesion segmentation in MR images. We present a novel automated lesion segmentation method consisting of an unsupervised mixture model based extraction of candidate lesion voxels, which are subsequently classified by a random decisi...

Accurate characterization of white-matter lesions from magnetic resonance (MR) images has increasing importance for diagnosis and management of treatment of certain neurological diseases, and can be performed in an objective and effective way by automated lesion segmentation. This usually involves modeling the whole-brain MR intensity distribution,...

Neuroimaging biomarkers are an important paraclinical tool used to characterize a number of neurological diseases, however, their extraction requires accurate and reliable segmentation of normal and pathological brain structures. For MR images of healthy brains the intensity models of normal-appearing brain tissue (NABT) in combination with Markov...

Mixture models are often used to compactly represent samples from heterogeneous sources. However, in real world, the samples generally contain an unknown fraction of outliers and the sources generate different or unbalanced numbers of observations. Such unbalanced and contaminated samples may, for instance, be obtained by high density data sensors...

Methods for automated segmentation of brain MR images are routinely used in large-scale neurological studies. Automated segmentation is usually performed by unsupervised methods, since these can be used even if different MR sequences or different pathologies are studied. The unsupervised methods model intensity distribution of major brain structure...

Diagnosis and prognosis of patients with multiple sclerosis (MS) rely on quantitative markers derived from the analysis of magnetic resonance (MR) images. To compute these markers, a segmentation of lesions in the brain tissues, which are characteristic for MS disease, is needed. In this paper, we propose an unsupervised method for segmenting MS le...