  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.
