DenseUNet for Breast Cancer Segmentation in Histopathological Images
Habib Rasi, Hossein Ebrahimnezhad, Mohammad Hossein Sedaaghi · 2026
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Description
Habib Rasi, Hossein Ebrahimnezhad, and Mohammad Hossein Sedaaghi test a 2026 deep network that outlines breast cancer in tissue images. Hand review of such images is slow and varies between readers. Their model, DenseUNet169, joins a DenseNet backbone with a UNet shape to mark cancer areas cell by cell.
Scores include overlap, pixel accuracy, and edge distance on a public data set. Results matched other backbone builds in the trials. The authors frame the work as a proof of concept, since the data set is small and far from proof for large-scale clinic use.
Catalog record
| CREATOR | Habib Rasi, Hossein Ebrahimnezhad, Mohammad Hossein Sedaaghi |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 56 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |