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- Ghoshal, Biraja, et al.
(författare)
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DeepHistoClass : A Novel Strategy for Confident Classification of Immunohistochemistry Images Using Deep Learning
- 2021
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Ingår i: Molecular & Cellular Proteomics. - : Elsevier. - 1535-9476 .- 1535-9484. ; 20
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Tidskriftsartikel (refereegranskat)abstract
- A multitude of efforts worldwide aim to create a single-cell reference map of the human body, for fundamental understanding of human health, molecular medicine, and targeted treatment. Antibody-based proteomics using immunohistochemistry (IHC) has proven to be an excellent technology for integration with large-scale single-cell transcriptomics datasets. The golden standard for evaluation of IHC staining patterns is manual annotation, which is expensive and may lead to subjective errors. Artificial intelligence holds much promise for efficient and accurate pattern recognition, but confidence in prediction needs to be addressed. Here, the aim was to present a reliable and comprehensive framework for automated annotation of IHC images. We developed a multilabel classification of 7848 complex IHC images of human testis corresponding to 2794 unique proteins, generated as part of the Human Protein Atlas (HPA) project. Manual annotation data for eight different cell types was generated as a basis for training and testing a proposed Hybrid Bayesian Neural Network. By combining the deep learning model with a novel uncertainty metric, DeepHistoClass (DHC) Confidence Score, the average diagnostic performance improved from 86.9% to 96.3%. This metric not only reveals which images are reliably classified by the model, but can also be utilized for identification of manual annotation errors. The proposed streamlined workflow can be developed further for other tissue types in health and disease and has important implications for digital pathology initiatives or large-scale protein mapping efforts such as the HPA project.
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| 2. |
- Ghoshal, Biraja, et al.
(författare)
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Estimating Uncertainty in Deep Learning for Reporting Confidence : An Application on Cell Type Prediction in Testes Based on Proteomics
- 2020
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Ingår i: Advances In Intelligent Data Analysis XVIII, IDA 2020. - Cham : Springer Nature. - 9783030445843 - 9783030445836 ; , s. 223-234
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Konferensbidrag (refereegranskat)abstract
- Multi-label classification in deep learning is a practical yet challenging task, because class overlaps in the feature space means that each instance is associated with multiple class labels. This requires a prediction of more than one class category for each input instance. To the best of our knowledge, this is the first deep learning study which quantifies uncertainty and model interpretability in multi-label classification; as well as applying it to the problem of recognising proteins expressed in cell types in testes based on immunohistochemically stained images. Multi-label classification is achieved by thresholding the class probabilities, with the optimal thresholds adaptively determined by a grid search scheme based on Matthews correlation coefficients. We adopt MC-Dropweights to approximate Bayesian Inference in multi-label classification to evaluate the usefulness of estimating uncertainty with predictive score to avoid overconfident, incorrect predictions in decision making. Our experimental results show that the MC-Dropweights visibly improve the performance to estimate uncertainty compared to state of the art approaches.
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