Radiological images have shown promising effects in patient prognostication. Deep learning provides a powerful approach for in-depth analysis of imaging data and integration of multi-modal data for modeling. In this work, we propose SurvivalCNN, a de...
BACKGROUND: Myocardial pathology segmentation plays an utmost role in the diagnosis and treatment of myocardial infarction (MI). However, manual segmentation is time-consuming and labor-intensive, and requires a lot of professional knowledge and clin...
IEEE transactions on bio-medical engineering
Oct 19, 2022
OBJECTIVE: Automated registration algorithms for a pair of 2D X-ray mammographic images taken from two standard imaging angles, namely, the craniocaudal (CC) and the mediolateral oblique (MLO) views, are developed.
IEEE transactions on bio-medical engineering
Oct 19, 2022
OBJECTIVE: This study introduces a deep learning approach to accurately predict challenging mechanical environments that possibly cause decreasing postural stability.
Aside from some living tissues, few materials can autonomously learn to exhibit desired behaviors as a consequence of prolonged exposure to unanticipated ambient loading scenarios. Still fewer materials can continue to exhibit previously learned beha...
Controlled environment agriculture (CEA) is an unconventional production system that is resource efficient, uses less space, and produces higher yields. Deep learning (DL) has recently been introduced in CEA for different applications including crop ...
Robot assembly tasks can fail due to unpredictable errors and can only continue with the manual intervention of a human operator. Recently, we proposed an exception strategy learning framework based on statistical learning and context determination, ...
Automated interictal epileptiform discharge (IED) detection has been widely studied, with machine learning methods at the forefront in recent years. As computational resources become more accessible, researchers have applied deep learning (DL) to IED...
Artificial neural networks have revolutionized electronic computing. Similarly, molecular networks with neuromorphic architectures may enable molecular decision-making on a level comparable to gene regulatory networks. Non-enzymatic networks could in...
Artificial Neural Network (ANN) is one of the powerful techniques of machine learning. It has shown its effectiveness in both prediction and classification problems. However, in some fields there is still some reticence towards their use mainly the f...
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