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#--- about page
import streamlit as st
description = "Home"
def run():
print("\nINFO (lit_home.run) loading ", description, " page ...")
#st.markdown('### Home')
#st.markdown('### Omdena Saudi Arabia')
#st.markdown('### Detecting Liver Cancer from Histopathology WSI Using Deep Learning and Explainability')
st.markdown('#### Background ')
st.markdown('\
Hepatocellular Carcinoma (HCC) is a primary liver malignancy, with \
alarming global impact. It is the 4th most common cause of cancer \
mortality worldwide, and the 6th most common malignancy overall. \
\
A patient\'s prognosis increases markedly with the speed of diagnosis \
and treatment, however the rates of occurrence are increasing at an \
alarming rate which will commensurately challenge the medical \
community. \
\
There are already several tools and technologies available to assist \
pathologists, however the current approach is ultimately constrained by \
a number of factors including: the rising demand, a limited supply \
of skilled specialists, the time required to grow/replenish this talent \
pool, and human factors which influence quality, accuracy, consistency, \
and speed (timeliness). \
')
st.markdown('#### Claim ')
st.markdown('\
It is the desire of this project team to increase the prognosis of \
hepatocellular cancer patients.\
\
Machine Learning techniques, specifically Deep Learning and \
Explainability (XAI) show promise in mimic\'ing the role of the \
pathologist. \
\
MLOps promises to establish a baseline for performance\
and a basis for continuous process improvement. This could greatly \
reduce human factor elements while accelerating the times and \
increasing the volumes of response.\
\
As a minimum, an ML application can serve as a supplement to the\
pathologist, a teaching aide, a verification tool, or as a framework\
for community collaboration and the advancement of quality diagnosis.\
')
st.markdown('#### Objectives ')
st.markdown('\
A key objective of this project is to produce a deployed app that will\
enable pathologists to upload a digital liver histopathology slide\
image and then receive an output that classifies the segment as\
malignant (or not). \
\
The utilization of Machine Learning and Explainability Techniques \
to the traditional process of Liver Histopathology and HCC Diagnosis \
could serve to greatly reduce the time to diagnosis and treatment. \
\
')
'''
st.markdown(
"""
Home page
""",
unsafe_allow_html=True,
)
<style>
# MainMenu {visibility: hidden;}
footer {visibility: hidden;}
</style>
''' |