Attention-based histological image analysis
With cancer being one of the leading causes of mortality worldwide, research into this field consistently features high up in priorities of the medical community. Hematoxylin & Eosin (H&E) stains represent the gold standard for medical diagnosis (NCI, n.d.). However, H&E-based cancer d...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Student Research Poster |
| Language: | English |
| Published: |
Nanyang Technological University
2024
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/180854 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | With cancer being one of the leading causes of mortality worldwide, research into this field consistently features high up in priorities of the medical community.
Hematoxylin & Eosin (H&E) stains represent the gold standard for medical diagnosis (NCI, n.d.). However, H&E-based cancer diagnosis is heavily dependent on the
visual judgement of pathologists, which constitutes both a bottleneck and a potential risk – that expert judgement will vary across pathologists (NHE, 2016).
Furthermore, manual assessments required as per current practice would result in high inter observer variability (Quaglia, 2018). Coupled with high cost and low
throughput, manual annotation of H&E images presents a major challenge in scaling up oncological research (Qaiser, 2018). Thus, this project seeks to automate this
process through a state-of-the-art deep learning model. |
|---|