Capturing fine-grained spatial details in brain tumor MRI via multi-module feature enhancement
Jianle Chen, Yuanyong Zhou, Hailin Cao, YuYan Lin, Jianyu Zhu, Huilian Liao · 2026
Read Capturing fine-grained spatial details in brain tumor MRI via multi-module feature enhancement by Jianle Chen, Yuanyong Zhou, Hailin Cao, YuYan Lin, Jianyu Zhu, Huilian Liao (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13556158.txt.
Description
Jianle Chen and five co-authors build a network to spot fine detail in brain tumour scans. Their paper is from two thousand twenty-six. Spot models in medical images can miss small traits, blend features weakly, and face skewed class counts. Their net, FDANet, builds on a YOLO frame and adds three modules.
One module draws fine space detail from tumour zones. A gate step lifts lesion traits while it damps background noise. A wavelet step blends features across scales. Heat maps and part-by-part tests back the design. On a mixed set of home and public scans, it picks out three tumour kinds with high scores, well past the base model, at low size and cost. Wider proof is still owed.
Catalog record
| CREATOR | Jianle Chen, Yuanyong Zhou, Hailin Cao, YuYan Lin, Jianyu Zhu, Huilian Liao |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 58 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |