UM Research Repository
metadata only
CoLM: Contrastive learning and multiple instance learning network for lung cancer classification of surgical options based on frozen pathological images
Histopathological images are regarded as the gold standard in cancer diagnosis. Formalin-fixed paraffin- embedded (FFPE) tissues are routinely collected and archived for pathological examination. However, the time-consuming procedures of tissue fixation and embedding render FFPE tissues unsuitable f
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
- Separability of histopathology foundation model embeddings of stimulated Raman histology images for lung carcinoma subtyping.#1134565
- HERO: Histology Encoder for Robust Representation in Oncology#1130785
- PCMamba: Integrating partial convolution and state space models for lung cancer histopathological image classification.#662814
- Postoperative Pulmonary Function Assessment Based on Deep Learning Study#1280654
- The American Cancer Society National Lung Cancer Roundtable strategic plan: Opportunities in advanced imaging across the lung cancer care continuum.#453000
Record · ID 1290975
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