Foundation models show promise in classifying atypical mitosis in cancer research

Researchers have benchmarked deep learning and vision foundation models for classifying atypical versus normal mitosis, a crucial indicator of tumor malignancy. The study evaluated end-to-end trained models, linear probing, and fine-tuning with LoRA across multiple datasets, including newly introduced ones. Results showed average balanced accuracies up to 0.81 on in-domain data and 0.77 on out-of-domain data, demonstrating the effectiveness of transfer learning techniques for this challenging classification task. AI

影响 Demonstrates improved accuracy in medical image classification using transfer learning, potentially aiding tumor malignancy assessment.

排序理由 Academic paper presenting a benchmark of deep learning models for a specific classification task.

在 arXiv cs.CV 阅读 →

• arXiv
• AtNorM-Br
• AtNorM-MD
• LoRA
• MIDOG++
• TCGA

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