Springer Nature OA
open access
CC BY 4.0
A Clinically Grounded Review of Medical Image Classification: Quantitative Insights into CNNs, Vision Transformers, and Hybrid CNN-ViT Models
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
- Deep Learning Classification of Uveal Melanoma and Metastatic Carcinoma on Cytology Specimens: A Single-Center Experience.#1147119
- Differentiation Benign and Malignant Pancreatic Lesions#835727
- From UNI2-h to ConvNeXt-T: Lightweight Nuclei Instance Segmentation via Knowledge Distillation#1129800
- MSLAU-Net: A Hybrid CNN-Transformer Network for Medical Image Segmentation#158720
- Lost in the Hype: Revealing and Dissecting the Performance Degradation of Medical Multimodal Large Language Models in Image Classification#2607
Record · ID 648536
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