  Pathological features are the gold standard for tumor diagnosis, guiding
treatment and prognosis. However, standard histopathological process is
labor-intensive and time-consuming, while frozen sections have lower accuracy.
Dynamic full-field optical coherence tomography (D-FFOCT) offers rapid
histologic information by measuring the subcellular dynamics of fresh,
unprocessed tissues. However, D-FFOCT images suffer from abrupt shifts in hue
and brightness, which is confusing for pathologists and diminish their
interpretability and reliability. Here, we present active phase
modulation-assisted D-FFOCT (APMD-FFOCT) to improve the imaging stability and
enhance the contrast of static tissues. This enables us to further employ an
unsupervised deep learning to convert APMD-FFOCT images into virtual
hematoxylin and eosin (H&E) stained images for the first time.
Three-dimensional (3D) virtual H&E-stained images have been obtained at a
scanning rate of 1 frame per second, as demonstrated in cancer diagnosis for
human central nervous system and breast. The results prove that this new method
will play a unique and important role in intraoperative histology.
