arXiv (All)
open access
Adaptive Bidirectional Task Interaction for Joint Segmentation and Classification of Breast Ultrasound
Joint lesion segmentation and tissue classification in breast ultrasound are usually trained with a shared encoder, so the two branches stop exchanging information once their decoders separate. That is exactly where boundary detail and semantic evidence are most complementary. The proposed method re
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
- US-JEPA: A Joint Embedding Predictive Architecture for Ultrasound#984998
- A deep learning framework for standardized interpretation of multiparameter cardiac ultrasound and disease classification.#436999
- Risk-Routed Implicit Boundary Refinement for Robust Ultrasound Image Segmentation#1356134
- Breast ultrasound images for segmentation and classification using multi-task U-Net.#340627
Record · ID 1217399
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