Natural Language Processing in Cancer:

                                           Extracting Diagnostic Insights from Pathology Reports

                                       PROJECT SPONSOR: National AI Campus 

Project Liaisons: 

Yimeng He and Xiuzhen Huang

Description: 

This project focuses on leveraging natural language processing (NLP) techniques to extract critical information from pathology reports.
Participants will gain hands-on experience with a wide range of text classification methods, from traditional TF-IDF analysis to cutting-edge large language models. The Cancer Genome Atlas (TCGA) pathology report corpus used in this project offers a unique opportunity for the development of advanced NLP technologies that can ultimately enhance patient diagnosis, treatment selection, and many other aspects of cancer care.

Objective:

Fall 2025:
- To develop an understanding of technology used in LLM building.
- We created multiple Jupyter notebooks to process data, optimize the accuracy of data classification and implemented an NLP using technically worded data. 

Spring 2026:
- To develop and implement new ideas based on our understanding of last semester this time with the cancer pathology datasets. TBA.  

Team Layout: 

Project Stack:

Meetings: