Search Results for author: Arcot Sowmya

Found 11 papers, 1 papers with code

hist2RNA: An efficient deep learning architecture to predict gene expression from breast cancer histopathology images

no code implementations10 Apr 2023 Raktim Kumar Mondol, Ewan K. A. Millar, Peter H Graham, Lois Browne, Arcot Sowmya, Erik Meijering

Gene expression can be used to subtype breast cancer with improved prediction of risk of recurrence and treatment responsiveness over that obtained using routine immunohistochemistry (IHC).

whole slide images

Temporal Pattern Mining for Analysis of Longitudinal Clinical Data: Identifying Risk Factors for Alzheimer's Disease

no code implementations11 Sep 2022 Annette Spooner, Gelareh Mohammadi, Perminder S. Sachdev, Henry Brodaty, Arcot Sowmya

This method uses temporal abstraction to convert the data into a more appropriate form for modelling, temporal pattern mining, to discover patterns in the complex, longitudinal data and machine learning models of survival analysis to select the discovered patterns.

Survival Analysis

Ensemble feature selection with clustering for analysis of high-dimensional, correlated clinical data in the search for Alzheimer's disease biomarkers

no code implementations6 Jul 2022 Annette Spooner, Gelareh Mohammadi, Perminder S. Sachdev, Henry Brodaty, Arcot Sowmya

When feature selection is applied to these datasets to identify the most important features, the biases inherent in some multivariate feature selectors due to correlated features make it difficult for these methods to distinguish between the important and irrelevant features and the results of the feature selection process can be unstable.

feature selection

Ensemble feature selection with data-driven thresholding for Alzheimer's disease biomarker discovery

no code implementations5 Jul 2022 Annette Spooner, Gelareh Mohammadi, Perminder S. Sachdev, Henry Brodaty, Arcot Sowmya

A fixed threshold, which is typically applied, offers no guarantee that the final set of selected features contains only relevant features.

feature selection

Multi-scale alignment and Spatial ROI Module for COVID-19 Diagnosis

no code implementations4 Jul 2022 Hongyan Xu, Dadong Wang, Arcot Sowmya

However, in CT and CXR images, the infected area occupies only a small part of the image.

COVID-19 Diagnosis

Organ localisation using supervised and semi supervised approaches combining reinforcement learning with imitation learning

no code implementations6 Dec 2021 Sankaran Iyer, Alan Blair, Laughlin Dawes, Daniel Moses, Christopher White, Arcot Sowmya

The results of experiments on localisation of the Spleen, Left and Right Kidneys in CT Images using supervised and semi supervised learning (SSL) demonstrate the ability to address data limitations with a much smaller data set and fewer annotations, compared to other state-of-the-art methods.

Imitation Learning Reinforcement Learning (RL)

Video Generative Adversarial Networks: A Review

no code implementations4 Nov 2020 Nuha Aldausari, Arcot Sowmya, Nadine Marcus, Gelareh Mohammadi

Then, a comprehensive review of video GANs models is provided under two main divisions according to the presence or non-presence of a condition.

Anomaly Detection

Brain tumour segmentation using cascaded 3D densely-connected U-net

no code implementations16 Sep 2020 Mina Ghaffari, Arcot Sowmya, Ruth Oliver

In this paper, we propose a deep-learning based method to segment a brain tumour into its subregions: whole tumour, tumour core and enhancing tumour.

Scalable Inference for Nonparametric Hawkes Process Using Pólya-Gamma Augmentation

no code implementations29 Oct 2019 Feng Zhou, Zhidong Li, Xuhui Fan, Yang Wang, Arcot Sowmya, Fang Chen

In this paper, we consider the sigmoid Gaussian Hawkes process model: the baseline intensity and triggering kernel of Hawkes process are both modeled as the sigmoid transformation of random trajectories drawn from Gaussian processes (GP).

Bayesian Inference Gaussian Processes +1

Efficient EM-Variational Inference for Hawkes Process

no code implementations29 May 2019 Feng Zhou, Zhidong Li, Xuhui Fan, Yang Wang, Arcot Sowmya, Fang Chen

In classical Hawkes process, the baseline intensity and triggering kernel are assumed to be a constant and parametric function respectively, which limits the model flexibility.

Variational Inference

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