NCBI PubMed Central
open access
STL-DeepBDC: A Robust Few-Shot Learning Framework for Multiclass Ovarian Tumor Classification in Ultrasound Outperforms Conventional Transfer Learning.
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
- N-Unet: An Efficient Multi-Task Model for Precise Classification and Segmentation of Breast Ultrasound Images.#232343
- Enhancing breast cancer detection accuracy through machine learning, deep learning and transfer learning techniques for clinical practice.#489469
- Deep Learning for Ultrasound Imaging: From Task-Specific Architectures to Foundation Models#651630
- Machine learning and deep learning models for predicting colorectal cancer metastases: A comprehensive review.#122840
- A cross data learning architecture for breast cancer classification using mammograms.#1001020
Record · ID 479501
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