ArticleScientific reports2019
ARA: accurate, reliable and active histopathological image classification framework with Bayesian deep learning.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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37 citing papers in PubMed.
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- Synergizing Deep Learning-Enabled Preprocessing and Human-AI Integration for Efficient Automatic Ground Truth Generation.Bioengineering (Basel, Switzerland) · 2024Article
- DiagSet: a dataset for prostate cancer histopathological image classification.Scientific reports · 2024Article
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- SnapEnsemFS: a snapshot ensembling-based deep feature selection model for colorectal cancer histological analysis.Scientific reports · 2023Article
- Effective and efficient active learning for deep learning-based tissue image analysis.Bioinformatics (Oxford, England) · 2023Article
- Tumor Cellularity Assessment of Breast Histopathological Slides via Instance Segmentation and Pathomic Features Explainability.Bioengineering (Basel, Switzerland) · 2023Article
- Tissue classification and diagnosis of colorectal cancer histopathology images using deep learning algorithms. Is the time ripe for clinical practice implementation?Przeglad gastroenterologiczny · 2023Review
- Accurate diagnostic tissue segmentation and concurrent disease subtyping with small datasets.Journal of pathology informatics · 2023Article
- Efficient Staining-Invariant Nuclei Segmentation Approach Using Self-Supervised Deep Contrastive Network.Diagnostics (Basel, Switzerland) · 2022Article
- Uncertainty-informed deep learning models enable high-confidence predictions for digital histopathology.Nature communications · 2022Article
- Exploring uncertainty measures in convolutional neural network for semantic segmentation of oral cancer images.Journal of biomedical optics · 2022Article
- Deep learning-based tumor microenvironment segmentation is predictive of tumor mutations and patient survival in non-small-cell lung cancer.BMC cancer · 2022Article
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4 authors.
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Abstract
Machine learning algorithms hold the promise to effectively automate the analysis of histopathological images that are routinely generated in clinical practice. Any machine learning method used in the clinical diagnostic process has to be extremely accurate and, ideally, provide a measure of uncertainty for its predictions. Such accurate and reliable classifiers need enough labelled data for training, which requires time-consuming and costly manual annotation by pathologists. Thus, it is critical to minimise the amount of data needed to reach the desired accuracy by maximising the efficiency of training. We propose an accurate, reliable and active (ARA) image classification framework and introduce a new Bayesian Convolutional Neural Network (ARA-CNN) for classifying histopathological images of colorectal cancer. The model achieves exceptional classification accuracy, outperforming other models trained on the same dataset. The network outputs an uncertainty measurement for each tested image. We show that uncertainty measures can be used to detect mislabelled training samples and can be employed in an efficient active learning workflow. Using a variational dropout-based entropy measure of uncertainty in the workflow speeds up the learning process by roughly 45%. Finally, we utilise our model to segment whole-slide images of colorectal tissue and compute segmentation-based spatial statistics.
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