Transformer vs CNNs: Colorectal Histology Classification Benchmark

A new study published on arXiv compares the performance of convolutional neural networks (CNNs), transformer-based models, and hybrid architectures for classifying colorectal histology images. The research evaluated twelve different models using the Kather dataset, finding that all models achieved high accuracy, with transformer architectures generally yielding the strongest results. While transformer-based models like EVA-02 and ViT-B/16 showed top performance, modern CNNs like ResNet34 and ConvNeXt-Tiny offered a competitive balance of accuracy and complexity. The study provides a benchmark for deep learning approaches in this specific area of computational pathology. AI

IMPACT Provides a benchmark for deep learning architectures in computational pathology, potentially guiding future research and development in medical image analysis.

RANK_REASON The cluster contains a research paper detailing a comparative study of deep learning models for a specific classification task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

• Colorectal Histology Classification
• ConvNeXt-Tiny
• EVA-02
• ImageNet
• Kather colorectal histopathology dataset
• ResNet34
• Transformer
• ViT-B/16

• paper
• other

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