  Histology slide digitization is becoming essential for telepathology (remote
consultation), knowledge sharing (education), and using the state-of-the-art
artificial intelligence algorithms (augmented/automated end-to-end clinical
workflows). However, the cumulative costs of digital multi-slide high-speed
brightfield scanners, cloud/on-premises storage, and personnel (IT and
technicians) make the current slide digitization workflows out-of-reach for
limited-resource settings, further widening the health equity gap; even
single-slide manual scanning commercial solutions are costly due to hardware
requirements (high-resolution cameras, high-spec PC/workstation, and support
for only high-end microscopes). In this work, we present a new cloud slide
digitization workflow for creating scanner-quality whole-slide images (WSIs)
from uploaded low-quality videos, acquired from cheap and inexpensive
microscopes with built-in cameras. Specifically, we present a pipeline to
create stitched WSIs while automatically deblurring out-of-focus regions,
upsampling input 10X images to 40X resolution, and reducing brightness/contrast
and light-source illumination variations. We demonstrate the WSI creation
efficacy from our workflow on World Health Organization-declared neglected
tropical disease, Cutaneous Leishmaniasis (prevalent only in the poorest
regions of the world and only diagnosed by sub-specialist dermatopathologists,
rare in poor countries), as well as other common pathologies on core biopsies
of breast, liver, duodenum, stomach and lymph node. The code and pretrained
models will be accessible via our GitHub
(https://github.com/nadeemlab/DeepLIIF), and the cloud platform will be
available at https://deepliif.org for uploading microscope videos and
downloading/viewing WSIs with shareable links (no sign-in required) for
telepathology and knowledge sharing.
