TDA-Net: Fusion of Persistent Homology and Deep Learning Features for COVID-19 Detection From Chest X-Ray Images

Hajij, Mustafa; Batayneh, Fawwaz

doi:10.1109/embc46164.2021.9629828

Cited by 17 publications

(15 citation statements)

References 22 publications

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“…In [32] the authors explain how a method that improves modern image classification techniques by considering topological features gave quite accurate results in classification of images using deep learning. Recently, the authors in [33] introduces a new method that talks about the fusion of TDA and Deep learning features for COVID-19 detection from chest X-Ray images.…”

Section: Persistent Homology Based Machine Learningmentioning

confidence: 99%

Machine learning techniques on homological persistence features for prostate cancer diagnosis

et al. 2022

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The rapid evolution of image processing equipment and techniques ensures the development of novel picture analysis methodologies. One of the most powerful yet computationally possible algebraic techniques for measuring the topological characteristics of functions is persistent homology. It's an algebraic invariant that can capture topological details at different spatial resolutions. Persistent homology investigates the topological features of a space using a set of sampled points, such as pixels. It can track the appearance and disappearance of topological features caused by changes in the nested space created by an operation known as filtration, in which a parameter scale, in our case the intensity of pixels, is increased to detect changes in the studied space over a range of varying scales. In addition, at the level of machine learning there were many studies and articles witnessing recently the combination between homological persistence and machine learning algorithms. On another level, prostate cancer is diagnosed referring to a scoring criterion describing the severity of the cancer called Gleason score. The classical Gleason system defines five histological growth patterns (grades). In our study we propose to study the Gleason score on some glands issued from a new optical microscopy technique called SLIM. This new optical microscopy technique that combines two classic ideas in light imaging: Zernike’s phase contrast microscopy and Gabor’s holography. Persistent homology features are computed on these images. We suggested machine learning methods to classify these images into the corresponding Gleason score. Machine learning techniques applied on homological persistence features was very effective in the detection of the right Gleason score of the prostate cancer in these kinds of images and showed an accuracy of above 95%.

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Section: Persistent Homology Based Machine Learningmentioning

confidence: 99%

Machine learning techniques on homological persistence features for prostate cancer diagnosis

et al. 2022

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“…DG-RNN [26] employed an attention module that takes long short-term memory (LSTM)'s output and models sequential medical events. To handle various healthcare tasks, TAdaNet [27] a meta-learning model makes use of a domain-knowledge graph to provide task-specific customization. These recent models are mostly focused on supervision, and their learning algorithms require labelled data.…”

Section: Related Workmentioning

confidence: 99%

Modeling electronic health record data using an end-to-end knowledge-graph-informed topic model

Zou

Pesaranghader

Verma

et al. 2022

Preprint

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The rapid growth of electronic health record (EHR) datasets opens up promising opportunities to understand human diseases in a systematic way. However, effective extraction of clinical knowledge from the EHR data has been hindered by its sparsity and noisy information. We present GAT-ETM, an end-to-end knowledge graph-based multimodal embedded topic model. GAT-ETM distills latent disease topics from EHR data by learning the embedding from a constructed medical knowledge graph. We applied GAT-ETM to a large-scale EHR dataset consisting of over 1 million patients. We evaluated its performance based on EHR reconstruction and drug imputation. GAT-ETM demonstrated superior performance over the alternative methods on both tasks. Moreover, our model learned clinically meaningful graph-informed embedding of the EHR codes. In additional, our model is also able to discover interpretable and accurate patient representations for patient stratification and drug recommendations. Our code is available at Anonymous GitHub.

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“…They have also been leveraged to distill dermoscopic images [29]. Bespoke input layers have been used to feed PH features to CNNs, improving electroencephalogram classification [30], and the detection of COVID-19 [31].…”

Section: Segmentation Topologymentioning

confidence: 99%

A Persistent Homology-Based Topological Loss Function for Multi-class CNN Segmentation of Cardiac MRI

Byrne

Clough

Montana

et al. 2021

Lecture Notes in Computer Science

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Multi-class segmentation of cardiac magnetic resonance (CMR) images seeks a separation of data into anatomical components with known structure and configuration. The most popular CNN-based methods are optimised using pixel wise loss functions, ignorant of the spatially extended features that characterise anatomy. Therefore, whilst sharing a high spatial overlap with the ground truth, inferred CNN-based segmentations can lack coherence, including spurious connected components, holes and voids. Such results are implausible, violating anticipated anatomical topology. In response, (single-class) persistent homologybased loss functions have been proposed to capture global anatomical features. Our work extends these approaches to the task of multi-class segmentation. Building an enriched topological description of all class labels and class label pairs, our loss functions make predictable and statistically significant improvements in segmentation topology using a CNN-based post-processing framework. We also present (and make available) a highly efficient implementation based on cubical complexes and parallel execution, enabling practical application within high resolution 3D data for the first time. We demonstrate our approach on 2D short axis and 3D whole heart CMR segmentation, advancing a detailed and faithful analysis of performance on two publicly available datasets.

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TDA-Net: Fusion of Persistent Homology and Deep Learning Features for COVID-19 Detection From Chest X-Ray Images

Cited by 17 publications

References 22 publications

Machine learning techniques on homological persistence features for prostate cancer diagnosis

Machine learning techniques on homological persistence features for prostate cancer diagnosis

Modeling electronic health record data using an end-to-end knowledge-graph-informed topic model

A Persistent Homology-Based Topological Loss Function for Multi-class CNN Segmentation of Cardiac MRI

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