Opportunities and obstacles for deep learning in biology and medicine

Ching, Travers; Himmelstein, Daniel; Beaulieu‐Jones, Brett K.; Kalinin, Alexandr A.; Brian, T.; Way, Gregory P.; Ferrero, Enrico; Agapow, Paul; Xie, Wei; Rosen, Gail; Lengerich, Benjamin J.; Israeli, Johnny; Lanchantin, Jack; Woloszynek, Stephen; Carpenter, Anne E.; Shrikumar, Avanti; Xu, Jinbo; Cofer, Evan M.; Harris, David J.; DeCaprio, Dave; Qi, Yanjun; Kundaje, Anshul; Peng, Yifan; Wiley, Laura K.; Segler, Marwin; Gitter, Anthony; Greene, Casey S.

doi:10.1101/142760

Cited by 236 publications

(257 citation statements)

References 370 publications

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“…We must note that optimizing neural networks is not straight forward. It requires the user to overcome obstacles such as overfitting, hyperparameter tuning, handling big data, and dealing with a shortage of, or imbalance in labeled data (18,45). Different methods to deal with these issues, such as transfer learning or data augmentation, need to be made accessible and easy to use.…”

Section: Discussionmentioning

confidence: 99%

Classification of Human White Blood Cells Using Machine Learning for Stain‐Free Imaging Flow Cytometry

et al. 2019

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Imaging flow cytometry (IFC) produces up to 12 spectrally distinct, information‐rich images of single cells at a throughput of 5,000 cells per second. Yet often, cell populations are still studied using manual gating, a technique that has several drawbacks, hence it would be advantageous to replace manual gating with an automated process. Ideally, this automated process would be based on stain‐free measurements, as the currently used staining techniques are expensive and potentially confounding. These stain‐free measurements originate from the brightfield and darkfield image channels, which capture transmitted and scattered light, respectively. To realize this automated, stain‐free approach, advanced machine learning (ML) methods are required. Previous works have successfully tested this approach on cell cycle phase classification with both a classical ML approach based on manually engineered features, and a deep learning (DL) approach. In this work, we compare both approaches extensively on the problem of white blood cell classification. Four human whole blood samples were assayed on an ImageStream‐X MK II imaging flow cytometer. Two samples were stained for the identification of eight white blood cell types, while two other sample sets were stained for the identification of resting and active eosinophils. For both data sets, four ML classifiers were evaluated on stain‐free imagery with stratified 5‐fold cross‐validation. On the white blood cell data set, the best obtained results were 0.778 and 0.703 balanced accuracy for classical ML and DL, respectively. On the eosinophil data set, this was 0.871 and 0.856 balanced accuracy. We conclude that classifying cell types based on only stain‐free images is possible with all four classifiers. Noteworthy, we also find that the DL approaches tested in this work do not outperform the approaches based on manually engineered features. © 2019 International Society for Advancement of Cytometry

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Section: Discussionmentioning

confidence: 99%

Classification of Human White Blood Cells Using Machine Learning for Stain‐Free Imaging Flow Cytometry

et al. 2019

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“…Machine learning algorithms are able to overcome these limitations as they do not require manual setting of the parameters, raising the research efforts on machine learning studies. In recent years, deep learning algorithms have become very popular, 40 and they will revolutionize health research. 40 Their increasing popularity is explained by the high accuracy in dealing with large multidimensional datasets and by their ability to perform automatic features extraction, making them very suitable to handle FCM data, where multidimensionality still represents a major issue during the data analysis.…”

Section: Future Per S Pec Tive Smentioning

confidence: 99%

Flow cytometry data analysis: Recent tools and algorithms

Montante

Brinkman

2019

Int J Lab Hematology

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Flow cytometry (FCM) allows scientists to rapidly quantify up to 50 parameters for millions of cells per sample. The bottleneck in the application of the technology is data analysis, and the high number of parameters measured by the current generation of instruments requires the use of advanced computational algorithms to make full use of their capabilities. This review summarizes the main steps of FCM data analysis, focusing on the use of the most recent bioinformatic tools developed for an R‐based programming environment. In particular, for each stage of the data analysis, libraries and packages currently available are listed, and a brief description of their functioning is included.

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“…Deep learning has shown remarkable success in medical and nonmedical image-classification tasks in the past 5 years, 1,2 finding its way into applications for digital pathology such as classification, cell detection, and segmentation. Based on these tasks, more abstract functions like disease grading, prognosis prediction, and imaging biomarkers for genetic subtype identification have been established.…”

Section: Introductionmentioning

confidence: 99%

Same same but different: A Web‐based deep learning application revealed classifying features for the histopathologic distinction of cortical malformations

et al. 2020

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Objective The microscopic review of hematoxylin‐eosin–stained images of focal cortical dysplasia type IIb and cortical tuber of tuberous sclerosis complex remains challenging. Both entities are distinct subtypes of human malformations of cortical development that share histopathological features consisting of neuronal dyslamination with dysmorphic neurons and balloon cells. We trained a convolutional neural network (CNN) to classify both entities and visualize the results. Additionally, we propose a new Web‐based deep learning application as proof of concept of how deep learning could enter the pathologic routine. Methods A digital processing pipeline was developed for a series of 56 cases of focal cortical dysplasia type IIb and cortical tuber of tuberous sclerosis complex to obtain 4000 regions of interest and 200 000 subsamples with different zoom and rotation angles to train a neural network. Guided gradient‐weighted class activation maps (Guided Grad‐CAMs) were generated to visualize morphological features used by the CNN to distinguish both entities. Results Our best‐performing network achieved 91% accuracy and 0.88 area under the receiver operating characteristic curve at the tile level for an unseen test set. Novel histopathologic patterns were found through the visualized Guided Grad‐CAMs. These patterns were assembled into a classification score to augment decision‐making in routine histopathology workup. This score was successfully validated by 11 expert neuropathologists and 12 nonexperts, boosting nonexperts to expert level performance. Significance Our newly developed Web application combines the visualization of whole slide images with the possibility of deep learning–aided classification between focal cortical dysplasia IIb and tuberous sclerosis complex. This approach will help to introduce deep learning applications and visualization for the histopathologic diagnosis of rare and difficult‐to‐classify brain lesions.

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Opportunities and obstacles for deep learning in biology and medicine

Cited by 236 publications

References 370 publications

Classification of Human White Blood Cells Using Machine Learning for Stain‐Free Imaging Flow Cytometry

Classification of Human White Blood Cells Using Machine Learning for Stain‐Free Imaging Flow Cytometry

Flow cytometry data analysis: Recent tools and algorithms

Same same but different: A Web‐based deep learning application revealed classifying features for the histopathologic distinction of cortical malformations

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