Research

Batch Effect Explorer for Imaging Analysis

[paper] [code] beex_overview BEEx is a pre-screening tool for image-based analyses that allows researchers to evaluate batch effects in multi-center studies and determine their origin and magnitude to facilitate development of accurate AI-based cancer models.

Wu Y, Xu X, Cheng Y, Zhang X, Liu F, Li Z, Hu L, Madabhushi A, Gao P, Liu Z, Lu C. BEEx Is an Open-Source Tool That Evaluates Batch Effects in Medical Images to Enable Multicenter Studies. Cancer Res. 2025 Jan 15;85(2):218-230. doi: 10.1158/0008-5472.CAN-23-3846. PMID: 39661030; PMCID: PMC11735318.

Separating Epithelial and Stromal Regions in Oral Cavity Squamous Cell Carcinomas

[paper] [code] oralcavity_abstract

The proposed model achieved a consistently accurate performance for epithelium segmentation.
The model trained on 10x magnification images achieved the best performance.
Morphologic features extracted from human- and AI-annotated epithelial regions are equivalent.

Wu Y, Koyuncu CF, Toro P, Corredor G, Feng Q, Buzzy C, Old M, Teknos T, Connelly ST, Jordan RC, Lang Kuhs KA, Lu C, Lewis JS Jr, Madabhushi A. A machine learning model for separating epithelial and stromal regions in oral cavity squamous cell carcinomas using H&E-stained histology images: A multi-center, retrospective study. Oral Oncol. 2022 Aug;131:105942. doi: 10.1016/j.oraloncology.2022.105942. Epub 2022 Jun 8. PMID: 35689952.