Diagnosis Of Takotsubo Syndrome By Robust Feature Selection From The Complex Latent Space Of DL-based Segmentation Network

Fahim Ahmed Zaman; Wahidul Alam; Tarun Kanti Roy; Amanda Chang; Kan Liu; Xiaodong Wu

doi:10.48550/arxiv.2312.12653

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Diagnosis Of Takotsubo Syndrome By Robust Feature Selection From The Complex Latent Space Of DL-based Segmentation Network

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Diagnosis Of Takotsubo Syndrome By Robust Feature Selection From The Complex Latent Space Of DL-based Segmentation Network

Fahim Ahmed Zaman, Wahidul Alam, Tarun Kanti Roy, Amanda Chang, Kan Liu and Xiaodong Wu

arXiv.org

Cornell University

12/19/2023

DOI: 10.48550/arxiv.2312.12653

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https://doi.org/10.48550/arxiv.2312.12653View

Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

Abstract

Researchers have shown significant correlations among segmented objects in various medical imaging modalities and disease related pathologies. Several studies showed that using hand crafted features for disease prediction neglects the immense possibility to use latent features from deep learning (DL) models which may reduce the overall accuracy of differential diagnosis. However, directly using classification or segmentation models on medical to learn latent features opt out robust feature selection and may lead to overfitting. To fill this gap, we propose a novel feature selection technique using the latent space of a segmentation model that can aid diagnosis. We evaluated our method in differentiating a rare cardiac disease: Takotsubo Syndrome (TTS) from the ST elevation myocardial infarction (STEMI) using echocardiogram videos (echo). TTS can mimic clinical features of STEMI in echo and extremely hard to distinguish. Our approach shows promising results in differential diagnosis of TTS with 82% diagnosis accuracy beating the previous state-of-the-art (SOTA) approach. Moreover, the robust feature selection technique using LASSO algorithm shows great potential in reducing the redundant features and creates a robust pipeline for short- and long-term disease prognoses in the downstream analysis.

Computer Science - Computer Vision and Pattern Recognition

Details

Title: Subtitle: Diagnosis Of Takotsubo Syndrome By Robust Feature Selection From The Complex Latent Space Of DL-based Segmentation Network
Creators: Fahim Ahmed Zaman
Wahidul Alam
Tarun Kanti Roy
Amanda Chang
Kan Liu
Xiaodong Wu
Resource Type: Preprint
Publication Details: arXiv.org
DOI: 10.48550/arxiv.2312.12653
eISSN: 2331-8422
Publisher: Cornell University; Ithaca, New York
Language: English
Date posted: 12/19/2023
Academic Unit: Electrical and Computer Engineering; Iowa Technology Institute; Cardiovascular Medicine; Radiation Oncology; The Iowa Institute for Biomedical Imaging; Internal Medicine
Record Identifier: 9984532054402771

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