Benchmarking Hierarchical Image Pyramid Transformer for the classification of colon biopsies and polyps in histopathology images
<|reference_start|>Benchmarking Hierarchical Image Pyramid Transformer for the classification of colon biopsies and polyps in histopathology images: Training neural networks with high-quality pixel-level annotation in histopathology whole-slide images (WSI) is an expensive process due to gigapixel resolution of WSIs. However, recent advances in self-supervised learning have shown that highly descriptive image representations can be learned without the need for annotations. We investigate the application of the recent Hierarchical Image Pyramid Transformer (HIPT) model for the specific task of classification of colorectal biopsies and polyps. After evaluating the effectiveness of TCGA-learned features in the original HIPT model, we incorporate colon biopsy image information into HIPT's pretraining using two distinct strategies: (1) fine-tuning HIPT from the existing TCGA weights and (2) pretraining HIPT from random weight initialization. We compare the performance of these pretraining regimes on two colorectal biopsy classification tasks: binary and multiclass classification.<|reference_end|>
@article{contreras2024benchmarking,
    title={Benchmarking Hierarchical Image Pyramid Transformer for the
  classification of colon biopsies and polyps in histopathology images},
    author={Nohemi Sofia Leon Contreras, Marina D'Amato, Francesco Ciompi, Clement
  Grisi, Witali Aswolinskiy, Simona Vatrano, Filippo Fraggetta, Iris Nagtegaal},
    journal={arXiv preprint arXiv:2405.15127},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.15127},
    primaryClass={eess.IV cs.AI cs.CV}
}