NCBI PubMed Central
open access
A patient-aware benchmarking of CNN and transformer architectures for breast cancer histopathology classification.
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- TransBreast-Net: An interpretable vision transformer ensemble for breast cancer histopathology classification.#362188
- Interpretable CRAM‑Enhanced Lightweight Dual‑Branch CNN for Real‑Time Breast Cancer Histopathology in Internet‑of‑Medical‑Things Environments.#165736
- Comprehensive Benchmarking of Deep Learning Architectures for Lung Cancer Histopathology#617038
- Comprehensive Benchmarking of Deep Learning Architectures for Lung Cancer Histopathology#616538
- Benchmarking Pathology Foundation Models for Breast Cancer Survival Prediction#138916
Record · ID 221209
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive