Computer Enhanced Analysis of Images from Immunohistochemistry
Overview
Due to the high prevalence of immunohistochemistry (IHC) in medicine and biology, the need of fast and accurate semi-quantitative analysis of such IHC images arises. Nowadays such analysis is performed manually by experts in the field but due to this fact the process depends on subjective factors and the large number of samples is provoked the creation of a solution designed to automate the process and the statistical processing of the results. The current research presents an innovative approach for performing subjective-free computer quantitative analysis and for the vast localization and clusterization of colored regions in IHC images which represent different kinds of antigens/antibodies, as well as comparison of IHC images, which is considered by experts as a big advantage since this could reduce the analysis time needed. The proposed methods in this research were applied in two independent medical researches, yielding significant results and positive feedback.
Competition history
- ISEF 2014
Resources
Related projects
ISEF · 2014
Quantitative Analysis of Macro-Cellular Biomarkers in Early Stage Ductal Carcinoma in situ (DCIS) Immunohistochemical Cytopathology Images Using Machine Learning
ISEF · 2025
Using Explainable AI in Immunohistochemistry Cell Images for Cancer Diagnosis
ISEF · 2015
Improved Cancer Detection and Diagnosis through a Novel Combination of Cell Segmentation and Artificial Intelligence Techniques
ISEF · 2025
Spatial Tumor-Immune Analysis: Insights From Pathology Slides and Breast Cancer Survival
ISEF · 2019
Optimizing Cell Quantification in Biological Assays Using a Convolutional Neural Network
ISEF · 2021
Revolutionizing Computer Vision Algorithms in Cancer Pathology: The Use of Comprehensive Toolkits to Overcome Machine Learning Obstacles in the Digital Pathology Field
ISEF · 2025
A Novel Method for Cancer Whole Slide Image Processing Utilizing a Multi-Scale Convolutional Residual Neural Network for Regression
ISEF · 2026
BiQancer: A Novel Quantitative Mathematical Biomarker and Automated End-to-End System for Decentralized Cervical Pre-Cancer Detection
Closest projects by meaning, across every fair and year in the corpus.
Browse more like this
Source: Regeneron International Science and Engineering Fair