Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images
importance 6/10
ai assisted
AI in cancer detection digital pathology stage: research
Problem
Automated detection of nuclei in high-resolution breast cancer histopathology images, needed for computer-assisted grading such as the Nottingham Histologic Score.
Why it matters
Accurate automated nuclei detection supports cancer grading and diagnosis, where manual analysis is slow and variable.
What the AI did
A Stacked Sparse Autoencoder, a deep learning strategy, was presented for efficient nuclei detection on high-resolution histopathological images.
Results
not stated in source
Impact
Peer-reviewed in a top medical imaging journal with 851 citations, indicating wide research influence.
Record
| Venue | IEEE Transactions on Medical Imaging |
|---|---|
| Published | 2015 |
| Work type | article |
| Authors | Jun Xu, Lei Xiang, Qingshan Liu, Hannah L. Gilmore, Jianzhong Wu, Tang Jinghai, Anant Madabhushi |
| Citations | 851 (OpenAlex, at import time) |
| References | 49 |
| Model or method | Stacked Sparse Autoencoder (SSAE) |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1109/tmi.2015.2458702 |
| Open access | green |
| Keywords | deep learning, autoencoder, nuclei detection, histopathology, breast cancer |
Importance rating
6
significant — thousands to millions affected
Automated nuclei detection supports breast cancer grading and diagnosis, a clinically significant task; abstract gives no scale or outcome figures.