Developing a Web App to Detect Hepatocellular Carcinoma Histopathology Using Deep Learning and XAI

This Omdena Local Chapter Challenge runs for 7 weeks and is a unique experience to try and grow your skills in a collaborative and safe environment with a diverse mix of people from all over the world. 

You will work on solving a local problem, initiated by Saudi Arabia Chapter. 

The problem

The incidence of Hepatocellular Carcinoma (HCC) is expected to increase dramatically in the next 30 years in Saudi Arabia (Abdo et al. 2012). Early detection of liver cancer is crucial for better prognosis and treatment outcomes:

• Liver cancer often does not cause any obvious symptoms in its early stages, which makes early detection through screening tests even more important.
• When liver cancer is diagnosed at an early stage, it is more likely to be treatable with curative intent.
• In people with early-stage liver cancers who have a liver transplant, the 5-year survival rate is in the range of 60% to 70%.
• On the other hand, liver cancer that is diagnosed at a later stage may require more aggressive treatments and can have a poor prognosis.

Early detection and treatment are critical in improving outcomes and increasing the chances of survival for liver cancer patients. Therefore, it is crucial to identify individuals who are at a higher risk for liver cancer and offer them appropriate screening tests. One way to improve the rate of early detection is by implementing high-accuracy, rapid, and efficient machine learning models. These models can aid in the early identification of liver cancer and prompt intervention. Additionally, explainability models can provide healthcare providers and patients with a better understanding of the diagnosis and the model’s methods. Ultimately, any liver cancer diagnosis is one too many, emphasizing the need for early detection and improved diagnostic tools.

The goals

The Omdena Saudi Arabia Chapter aims to develop an app based on Deep Learning models to predict liver cancer (hepatocellular carcinoma or HCC) from whole slide histopathology images (WSI). The project’s primary goal is to accurately classify digital pathology images as healthy or malignant, with an XAI (Explainability model) with a deployed web app.

With a duration of 7-weeks, this project aims to achieve:

• Data Collection and Exploratory Data Analysis
• Preprocessing 
• Feature Extraction
• Model Development and Training
• Evaluate Model
• App development
• App deployment
• Research paper for publication
• Project Presentation

Why join? The uniqueness of Omdena Local Chapter Challenges

Omdena Local Chapter Challenges are not a competition or hackathon but a real-world project that will grow your experience to a new level.

A unique learning experience with the potential to make an impact through the outcome of the project. You will go through an entire data science project lifecycle. This covers problem scoping, data collection, and preparation, as well as modeling for deployment.

And the best part is that you will join the global and collaborative community of Omdena with tons of benefits to accelerate your career.

Read more on how Omdena´s Local Chapters work