  Intensity diffraction tomography (IDT) refers to a class of optical
microscopy techniques for imaging the 3D refractive index (RI) distribution of
a sample from a set of 2D intensity-only measurements. The reconstruction of
artifact-free RI maps is a fundamental challenge in IDT due to the loss of
phase information and the missing cone problem. Neural fields (NF) has recently
emerged as a new deep learning (DL) approach for learning continuous
representations of physical fields. NF uses a coordinate-based neural network
to represent the field by mapping the spatial coordinates to the corresponding
physical quantities, in our case the complex-valued refractive index values. We
present DeCAF as the first NF-based IDT method that can learn a high-quality
continuous representation of a RI volume from its intensity-only and
limited-angle measurements. The representation in DeCAF is learned directly
from the measurements of the test sample by using the IDT forward model,
without any ground-truth RI maps. We qualitatively and quantitatively evaluate
DeCAF on the simulated and experimental biological samples. Our results show
that DeCAF can generate high-contrast and artifact-free RI maps and lead to up
to 2.1 times reduction in MSE over existing methods.
