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:10.1109/ISBI56570.2024.10635574

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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

2024 IEEE International Symposium on Biomedical Imaging (ISBI), pp.1-5

05/27/2024

DOI: 10.1109/ISBI56570.2024.10635574

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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.

Noise

Pathology

Accuracy

Disease classification

Echo

Feature extraction

Feature selection

Image segmentation

Pipelines

Predictive models

Segmentation latent space

Takotsubo Syndrome

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 - University of Iowa
Wahidul Alam - University of Iowa
Tarun Kanti Roy - University of Iowa
Amanda Chang - University of Iowa
Kan Liu - University of Iowa
Xiaodong Wu - University of Iowa
Resource Type: Conference proceeding
Publication Details: 2024 IEEE International Symposium on Biomedical Imaging (ISBI), pp.1-5
Publisher: IEEE
DOI: 10.1109/ISBI56570.2024.10635574
eISSN: 1945-8452
Language: English
Date published: 05/27/2024
Academic Unit: The Iowa Institute for Biomedical Imaging; Electrical and Computer Engineering; Cardiovascular Medicine; Internal Medicine; Iowa Technology Institute; Radiation Oncology
Record Identifier: 9984698938902771

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