Optical coherence tomography (OCT) has now become a standard of care,
impacting the treatment of millions of people every year. There is tremendous clinical and
preclinical OCT progress in diagnosing cancers and disorders in ophthalmology,
cardiology, neurology, dermatology, gastroenterology, etc. In this talk, deep learning
algorithms for detecting/segmenting the crucial cell/tissue/lesion features, such as nuclei,
the dermal-epidermal junction of human skin, and tumor boundaries, will be addressed.
The performance can be explained by visualizing the neural network's feature activations
in response to the cell-like structure of human tissues. Histopathological stained images are
considered the gold standard for clinical cancer diagnosis. However, the staining
processing time is long, especially when surgery progresses. There is an unmet need to
build an image translation model to convert the grey-level OCT images to mimic the
stained images. Both semi-supervised and unsupervised approaches toward virtue
histopathology will be addressed. Leveraging the ever-escalating techniques in applying
deep learning algorithms to medical image analysis could accelerate the acceptance of deep
learning applications among clinicians and patients.

Co-sponsored by: Tata Institute of Fundamental Research-ASET Colloquim

Speaker(s): Prof. Sheng-Lung Huang, ,

Bldg: Colaba, TIFR Auditorium, Mumbai, Maharashtra, India, 400005