A generalized deep learning framework for whole-slide image segmentation and analysis
importance 7/10
ai assisted
AI in cancer detection digital pathology stage: research
Problem
Analyzing whole-slide histopathology images for cancer diagnosis is time-consuming because of their large size, heterogeneity, and rising case numbers.
Why it matters
Histopathology tissue analysis is the gold standard for cancer diagnosis and prognosis.
What the AI did
Proposed a generalized deep learning-based framework for automated segmentation and analysis of whole-slide images.
Results
not stated in source
Impact
Aims to improve precision, speed and reproducibility of whole-slide image analysis.
Record
| Venue | Scientific Reports |
|---|---|
| Published | 2021 |
| Work type | article |
| Authors | Mahendra Khened, Avinash Kori, Haran Rajkumar, Ganapathy Krishnamurthi, Balaji Srinivasan |
| Citations | 239 (OpenAlex, at import time) |
| References | 69 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1038/s41598-021-90444-8 |
| Open access | gold |
| Keywords | histopathology, whole-slide imaging, segmentation, deep learning, cancer diagnosis |
Importance rating
7
significant — thousands to millions affected
Histopathology is the gold standard for cancer diagnosis and prognosis; abstract notes rising potential cancer cases and worldwide WSI adoption in pathology labs.