Related Experiment Video
Updated: Dec 29, 2025
12:03
Multi-photon Imaging of Tumor Cell Invasion in an Orthotopic Mouse Model of Oral Squamous Cell Carcinoma
Published on: July 25, 2011
19.9K
Histopathological imaging database for oral cancer analysis.
Tabassum Yesmin Rahman1, Lipi B Mahanta2, Anup K Das3
1Department of Computer Science & IT, Cotton University, Panbazar, Guwahati, Assam, 781001, India.
Data in Brief
|February 6, 2020
Summary
This study presents a dataset of 1224 histopathological images of oral cavity normal epithelium and Oral Squamous Cell Carcinoma (OSCC). This resource supports the development of AI-driven diagnostic tools for more objective cancer detection.
Area of Science:
- Pathology
- Medical Imaging
- Artificial Intelligence
Background:
- Histopathology is crucial for disease diagnosis, particularly cancer, relying on microscopic examination of tissue biopsies.
- Current methods using biopsies may lack uniformity and reproducibility due to subjective criteria.
- Computational tools offer objective, quantitative measures for diagnostic assessments.
Purpose of the Study:
- To introduce a comprehensive dataset of histopathological images for Oral Squamous Cell Carcinoma (OSCC) research.
- To facilitate the development of automated diagnostic systems leveraging Artificial Intelligence (AI).
Main Methods:
- A repository of 1224 H&E stained histopathological images from 230 patients was curated.
- Images include normal oral epithelium and OSCC at 100x and 400x magnifications.
- A subset was previously used for OSCC detection based on textural features.
Main Results:
- The dataset comprises 1224 images, divided into two sets with varying resolutions (100x and 400x).
- Includes 1080 OSCC images and 290 normal oral epithelium images.
- The data was collected using a Leica ICC50 HD microscope and prepared by medical experts.
Conclusions:
- This dataset provides a valuable resource for training and validating AI algorithms for OSCC diagnosis.
- Enables the creation of objective, reproducible, and potentially more accurate diagnostic tools.
- Aims to advance computational pathology in oral cancer detection.
