WEEP: A method for spatial interpretation of weakly supervised CNN models in computational pathology
<|reference_start|>WEEP: A method for spatial interpretation of weakly supervised CNN models in computational pathology: Deep learning enables the modelling of high-resolution histopathology whole-slide images (WSI). Weakly supervised learning of tile-level data is typically applied for tasks where labels only exist on the patient or WSI level (e.g. patient outcomes or histological grading). In this context, there is a need for improved spatial interpretability of predictions from such models. We propose a novel method, Wsi rEgion sElection aPproach (WEEP), for model interpretation. It provides a principled yet straightforward way to establish the spatial area of WSI required for assigning a particular prediction label. We demonstrate WEEP on a binary classification task in the area of breast cancer computational pathology. WEEP is easy to implement, is directly connected to the model-based decision process, and offers information relevant to both research and diagnostic applications.<|reference_end|>
@article{sharma2024weep:,
    title={WEEP: A method for spatial interpretation of weakly supervised CNN
  models in computational pathology},
    author={Abhinav Sharma, Bojing Liu, Mattias Rantalainen},
    journal={arXiv preprint arXiv:2403.15238},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.15238},
    primaryClass={eess.IV cs.CV stat.ME}
}