RESEARCH & INTERESTS
Computer Vision, Machine Learning, Artificial Intelligence, Biomedical Informatics, Clinical Data Analysis, Deep Learning, Data Mining, Image Processing, Digital Pathology, Whole Slide Image Processing, Detecting Cancer, Survival Analysis.
MACHINE LEARNING & BIOMEDICAL INFORMATICS
Our research focuses on the applications of machine learning in multidisciplinary research areas. Predicting clinical outcomes from biomedical data through quantitative phenotypic information from digital pathology images has received much attention. Our work has contributed to establishing many open software tools that enable researchers to handle large and complex datasets using machine learning algorithms quickly.
Hybrid Interactive Machine Learning Tool (HIMLT)
HIMLT is an interactive machine learning software tool that provides fusion-based classification via interactive training within a unified digital pathology platform.
Source
https://github.com/MachineVisionTeam/HistomicsHIMLT
Documentation
https://github.com/MachineVisionTeam/HistomicsHIMLT/blob/main/docs/DEPLOYMENT.md
HistomicsML
HistomicsML is an active machine learning tool that enables researchers to manage whole slide images very quickly and precisely.
Source
https://github.com/CancerDataScience/HistomicsML2
Documentation
HistomicsTK
HistomicsTK is a Python and REST API for the analysis of Histopathology images in association with clinical and genomic data.
Source
https://github.com/DigitalSlideArchive/HistomicsTK
Documentation
https://digitalslidearchive.github.io/HistomicsTK/