To calculate the light propagation through heterogeneous bio-tissues, we propose a
convolutional deep network with specified convolutional kernels and calculation rules. The …

Significance: Deep learning (DL) models are being increasingly developed to map sensor
data to the image domain directly. However, DL methodologies are data-driven and require …

Diffuse optical imaging is an emerging modality that uses Near Infrared (NIR) light to reveal
structural and functional information of deep biological tissue. It provides contrast …

Diffuse optical tomography (DOT) is a noninvasive biomedical imaging method to
reconstruct optical property distribution. Since the underdetermined characteristic of …

Deep learning has been actively investigated for various applications such as image
classification, computer vision, and regression tasks, and it has shown state-of-the-art …

FREEnet: a dynamic deep-learning model for freehand diffuse optical tomography Page 1
FREEnet: a dynamic deep-learning model for freehand diffuse optical tomography Robin …

Diffuse optical tomography using deep learning is an emerging technology that has found
impressive medical diagnostic applications. However, creating an optical imaging system …

Learning-based methods have been proved to perform well in a variety of areas in the
biomedical field, such as biomedical image segmentation, and histopathological image …

We proposed and implemented a deep learning scheme using convolution neural networks
(CNNs) with batch normalization (BNCNN) to construct a sensor-image DOI computation …

A machine learning model with physical constraints (ML-PC) is introduced to perform diffuse
optical tomography (DOT) reconstruction. DOT reconstruction is an ill-posed and under …