Length-scale study in deep learning prediction for non-small cell lung cancer brain metastasis.

ABSTRACT: Deep learning-assisted digital pathology has demonstrated the potential to profoundly impact clinical practice, even surpassing human pathologists in performance. However, as deep neural network (DNN) architectures grow in size and complexity, their explainability decreases, posing challenges in interpreting pathology features for broader clinical insights into physiological diseases. To better assess the interpretability of digital microscopic images and guide future microscopic system design, we developed a novel method to study the predictive feature length-scale that underpins a DNN's predictive power. We applied this method to analyze a DNN's capability in predicting brain metastasis from early-stage non-small-cell lung cancer biopsy slides. This study quantifies DNN's attention for b

SUBMITTER: Zhou H 

PROVIDER: S-EPMC11436900 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature