# Deep Learning for Whole-Slide Tissue Histopathology Classification: A Comparative Study in the Identification of Dysplastic and Non-Dysplastic Barrett’s Esophagus

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## Abstract

**:**

## 1. Background

## 2. Materials and Methods

#### 2.1. Data Collection

#### 2.2. Esophageal Biopsy Datasets

#### 2.3. Deep Learning-Based Feature Representation

#### 2.3.1. Fully Supervised Feature Learning

#### 2.3.2. Unsupervised Feature Learning

#### Codebook Learning

#### WSI Encoding

#### 2.3.3. Weakly Supervised Feature Learning

#### Multiple Instance Learning

#### Expectation-Maximization Model

#### 2.4. Slide-Level Inference

#### 2.5. Feature Importance

## 3. Experimental Evaluation

#### 3.1. Patch Extraction

#### 3.2. Deep Models Architecture

#### 3.3. Experimental Setup

- FS-RF: Image features were extracted using the fully supervised approach, and the random forest was employed for image-level decision fusion;
- FS-SVM: Image features were extracted using the fully supervised approach, and SVM was employed for image-level decision fusion;
- MIL-RF: Image features were extracted using the MIL approach, and the random forest was employed for image-level decision fusion;
- MIL-SVM: Image features were extracted using the MIL approach, and SVM was employed for image-level decision fusion;
- EM-RF: Image features were extracted using the EM approach, and the random forest was employed for image-level decision fusion;
- EM-SVM: Image features were extracted using the EM approach, and SVM was employed for image-level decision fusion;
- KM-RF: Image features were extracted using an unsupervised approach that applies the k-means clustering algorithm to learn codewords. This model employs the random forest for image-level decision fusion;
- KM-SVM: Image features were extracted using an unsupervised approach that applies the k-means clustering algorithm to learn codewords. This model employs SVM for image-level decision fusion;
- GMM-RF: Image features were extracted using an unsupervised approach that applies the GMM clustering algorithm to learn codewords. This model employs the random forest for image-level decision fusion;
- GMM-SVM: Image features were extracted using an unsupervised approach that applies the GMM clustering algorithm to learn codewords. This model employs SVM for image-level decision fusion.

#### 3.4. Classification Results and Statistical Analysis

#### 3.5. Visualization of ROIs on WSIs

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## Abbreviations

BE | Barrett’s Esophagus |

WSI | Whole-Slide Images |

H&E | Hematoxylin and Eosin |

HD-WLE | High-Definition White-Light Endoscopy |

NBI | Narrow Band Imaging |

CNN | Convolutional Neural Networks |

SIFT | Scale-Invariant Feature Transform |

CAE | Convolutional Auto-Encoder |

GMM | Gaussian Mixture Model |

TF | Term Frequencies |

MIL | Multiple Instance Learning |

EM | Expectation-Maximization |

ROI | Regions of Interest |

FS | Fully Supervised |

RF | Random Forest |

KM | k-Means Clustering |

AUC | Area Under the ROC Curve |

PCA | Principle Components Analysis |

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**Figure 1.**An example of the annotation process on a typical whole-slide image (WSI). Red, green, and yellow highlighted areas indicate areas that were annotated and from which labeled patches were taken. Squamous tissue (green arrowhead), non-dysplastic Barrett’s with Goblet cells (yellow arrowhead), and dysplastic tissue with crowding and hyperchromasia (lower zoomed section) were all present within the same whole-slide image.

**Figure 2.**Overview of the fully supervised feature extraction framework. (

**A**) A convolutional neural network (CNN) is trained on high-resolution tissue tiles sampled from annotated regions of WSIs in the training set. (

**B**) Next, the trained model is employed to output the class’ probability distributions for each high-resolution tissue tile generated from new WSIs. The patch-level probabilities corresponding to all patches derived from a WSI are aggregated into WSI-level probabilities histogram.

**Figure 3.**Overview of the unsupervised feature extraction framework. (

**A**) This is the codebook learning phase. In this step, tissue tiles are sampled from WSIs in the training set. Neither the annotated areas nor the WSIs labels are used in this framework. An encoder is trained in an unsupervised fashion to map each high-resolution tissue tile into a low-dimensional embedding space, and then the Gaussian mixture model (GMM) is employed to cluster extracted features from tissue tiles into a number of clusters. Each cluster is indeed a morphological feature called a codeblock. The set of all codeblocks is called codebook. (

**B**) This is the WSI encoding phase. In this phase, the trained convolutional autoencoder (CAE) is employed to extract embedding features from high-resolution tissue tiles derived from new WSIs. Then, the posterior probabilities of clusters constructed in the previous phase for patch-level extracted features are calculated. Finally, the posterior probabilities corresponding to all patches derived from a WSI are aggregated into a WSI-level probabilities histogram.

**Figure 4.**Overview of weakly supervised feature extraction framework. (

**A**) A CNN is trained on high-resolution tissue tiles sampled from the labeled WSIs in the training set. This model uses only the reported diagnoses as labels for training WSIs and assumes that sampled tissue tiles have the same labels as their corresponding WSIs. (

**B**) Once the training concludes, the trained model is employed to output the class’ probability distributions for each high-resolution tissue tile generated from new WSIs. The patch-level probabilities corresponding to all patches derived from a WSI are aggregated into a WSI-level probabilities histogram.

**Figure 5.**Principle component analysis (PCA) plot for WSIs encoded using (

**A**) fully supervised, (

**B**) multiple instance learning (MIL), (

**C**) expectation-maximization (EM), (

**D**) unsupervised (k-means), and (

**E**) unsupervised (GMM) approaches.

**Figure 6.**Boxplots of the 10-fold cross-validation results for weighted (

**A**) accuracy, (

**B**) area under the ROC curve (AUC), (

**C**) precision, (

**D**) recall, and (

**E**) F1 score in different models.

**Figure 7.**Heatmaps generated by different feature extraction approaches for some samples from (

**A**) dysplastic Barrett’s esophagus (BE) and (

**B**) non-dysplastic BE. Area of attention is shown in red.

Feature Extraction Approach | Clustering Algorithm | Classification Algorithm | Model |
---|---|---|---|

fully supervised | - | Random Forest | FS-RF |

fully supervised | - | SVM | FS-SVM |

weakly supervised (MIL) | - | Random Forest | MIL-RF |

weakly supervised (MIL) | - | SVM | MIL-SVM |

weakly supervised (EM) | - | Random Forest | EM-RF |

weakly supervised (EM) | - | SVM | EM-SVM |

unsupervised | k-means | Random Forest | KM-RF |

unsupervised | k-means | SVM | KM-SVM |

unsupervised | GMM | Random Forest | GMM-RF |

unsupervised | GMM | SVM | GMM-SVM |

Models | Classes | Metrices | ||||
---|---|---|---|---|---|---|

Accuracy | AUC | Precision | Recall | F1 score | ||

FS-RF | Dysplastic BE | $0.563\phantom{\rule{3.33333pt}{0ex}}[0.454,\phantom{\rule{3.33333pt}{0ex}}0.671]$ | $0.616\phantom{\rule{3.33333pt}{0ex}}[0.489,\phantom{\rule{3.33333pt}{0ex}}0.743]$ | $0.395\phantom{\rule{3.33333pt}{0ex}}[0.211,\phantom{\rule{3.33333pt}{0ex}}0.578]$ | $0.451\phantom{\rule{3.33333pt}{0ex}}[0.319,\phantom{\rule{3.33333pt}{0ex}}0.584]$ | $0.367\phantom{\rule{3.33333pt}{0ex}}[0.261,\phantom{\rule{3.33333pt}{0ex}}0.472]$ |

Non-dysplastic BE | $0.655\phantom{\rule{3.33333pt}{0ex}}[0.563,\phantom{\rule{3.33333pt}{0ex}}0.746]$ | $0.758\phantom{\rule{3.33333pt}{0ex}}[0.653,\phantom{\rule{3.33333pt}{0ex}}0.863]$ | $0.565\phantom{\rule{3.33333pt}{0ex}}[0.360,\phantom{\rule{3.33333pt}{0ex}}0.770]$ | $0.555\phantom{\rule{3.33333pt}{0ex}}[0.384,\phantom{\rule{3.33333pt}{0ex}}0.726]$ | $0.520\phantom{\rule{3.33333pt}{0ex}}[0.360,\phantom{\rule{3.33333pt}{0ex}}0.680]$ | |

Squamous | $0.857\phantom{\rule{3.33333pt}{0ex}}[0.755,\phantom{\rule{3.33333pt}{0ex}}0.958]$ | $0.934\phantom{\rule{3.33333pt}{0ex}}[0.866,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.760\phantom{\rule{3.33333pt}{0ex}}[0.565,\phantom{\rule{3.33333pt}{0ex}}0.954]$ | $0.732\phantom{\rule{3.33333pt}{0ex}}[0.566,\phantom{\rule{3.33333pt}{0ex}}0.898]$ | $0.720\phantom{\rule{3.33333pt}{0ex}}[0.558,\phantom{\rule{3.33333pt}{0ex}}0.881]$ | |

Weighted Average | $0.655\phantom{\rule{3.33333pt}{0ex}}[0.560,\phantom{\rule{3.33333pt}{0ex}}0.751]$ | $0.761\phantom{\rule{3.33333pt}{0ex}}[0.647,\phantom{\rule{3.33333pt}{0ex}}0.876]$ | $0.655\phantom{\rule{3.33333pt}{0ex}}[0.507,\phantom{\rule{3.33333pt}{0ex}}0.802]$ | $0.537\phantom{\rule{3.33333pt}{0ex}}[0.416,\phantom{\rule{3.33333pt}{0ex}}0.657]$ | $0.554\phantom{\rule{3.33333pt}{0ex}}[0.430,\phantom{\rule{3.33333pt}{0ex}}0.678]$ | |

FS-SVM | Dysplastic BE | $0.619\phantom{\rule{3.33333pt}{0ex}}[0.480,\phantom{\rule{3.33333pt}{0ex}}0.758]$ | $0.661\phantom{\rule{3.33333pt}{0ex}}[0.481,\phantom{\rule{3.33333pt}{0ex}}0.840]$ | $0.434\phantom{\rule{3.33333pt}{0ex}}[0.246,\phantom{\rule{3.33333pt}{0ex}}0.622]$ | $0.565\phantom{\rule{3.33333pt}{0ex}}[0.379,\phantom{\rule{3.33333pt}{0ex}}0.751]$ | $0.455\phantom{\rule{3.33333pt}{0ex}}[0.303,\phantom{\rule{3.33333pt}{0ex}}0.607]$ |

Non-dysplastic BE | $0.663\phantom{\rule{3.33333pt}{0ex}}[0.551,\phantom{\rule{3.33333pt}{0ex}}0.775]$ | $0.778\phantom{\rule{3.33333pt}{0ex}}[0.665,\phantom{\rule{3.33333pt}{0ex}}0.892]$ | $0.542\phantom{\rule{3.33333pt}{0ex}}[0.323,\phantom{\rule{3.33333pt}{0ex}}0.762]$ | $0.418\phantom{\rule{3.33333pt}{0ex}}[0.162,\phantom{\rule{3.33333pt}{0ex}}0.673]$ | $0.424\phantom{\rule{3.33333pt}{0ex}}[0.217,\phantom{\rule{3.33333pt}{0ex}}0.632]$ | |

Squamous | $0.888\phantom{\rule{3.33333pt}{0ex}}[0.811,\phantom{\rule{3.33333pt}{0ex}}0.964]$ | $0.921\phantom{\rule{3.33333pt}{0ex}}[0.842,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.741\phantom{\rule{3.33333pt}{0ex}}[0.565,\phantom{\rule{3.33333pt}{0ex}}0.916]$ | $0.860\phantom{\rule{3.33333pt}{0ex}}[0.710,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.779\phantom{\rule{3.33333pt}{0ex}}[0.634,\phantom{\rule{3.33333pt}{0ex}}0.923]$ | |

Weighted Average | $0.689\phantom{\rule{3.33333pt}{0ex}}[0.593,\phantom{\rule{3.33333pt}{0ex}}0.784]$ | $0.773\phantom{\rule{3.33333pt}{0ex}}[0.644,\phantom{\rule{3.33333pt}{0ex}}0.902]$ | $0.637\phantom{\rule{3.33333pt}{0ex}}[0.466,\phantom{\rule{3.33333pt}{0ex}}0.807]$ | $0.585\phantom{\rule{3.33333pt}{0ex}}[0.448,\phantom{\rule{3.33333pt}{0ex}}0.721]$ | $0.572\phantom{\rule{3.33333pt}{0ex}}[0.423,\phantom{\rule{3.33333pt}{0ex}}0.721]$ | |

MIL-RF | Dysplastic BE | $0.842\phantom{\rule{3.33333pt}{0ex}}[0.739,\phantom{\rule{3.33333pt}{0ex}}0.945]$ | $0.924\phantom{\rule{3.33333pt}{0ex}}[0.835,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.756\phantom{\rule{3.33333pt}{0ex}}[0.584,\phantom{\rule{3.33333pt}{0ex}}0.929]$ | $0.761\phantom{\rule{3.33333pt}{0ex}}[0.526,\phantom{\rule{3.33333pt}{0ex}}0.996]$ | $0.694\phantom{\rule{3.33333pt}{0ex}}[0.524,\phantom{\rule{3.33333pt}{0ex}}0.863]$ |

Non-dysplastic BE | $0.844\phantom{\rule{3.33333pt}{0ex}}[0.742,\phantom{\rule{3.33333pt}{0ex}}0.945]$ | $0.926\phantom{\rule{3.33333pt}{0ex}}[0.835,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.816\phantom{\rule{3.33333pt}{0ex}}[0.657,\phantom{\rule{3.33333pt}{0ex}}0.976]$ | $0.820\phantom{\rule{3.33333pt}{0ex}}[0.710,\phantom{\rule{3.33333pt}{0ex}}0.930]$ | $0.793\phantom{\rule{3.33333pt}{0ex}}[0.692,\phantom{\rule{3.33333pt}{0ex}}0.893]$ | |

Squamous | $0.990\phantom{\rule{3.33333pt}{0ex}}[0.978,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $1.000\phantom{\rule{3.33333pt}{0ex}}[0.999,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.964\phantom{\rule{3.33333pt}{0ex}}[0.899,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.984\phantom{\rule{3.33333pt}{0ex}}[0.955,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.971\phantom{\rule{3.33333pt}{0ex}}[0.933,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | |

Weighted Average | $0.874\phantom{\rule{3.33333pt}{0ex}}[0.778,\phantom{\rule{3.33333pt}{0ex}}0.971]$ | $0.939\phantom{\rule{3.33333pt}{0ex}}[0.856,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.877\phantom{\rule{3.33333pt}{0ex}}[0.781,\phantom{\rule{3.33333pt}{0ex}}0.974]$ | $0.838\phantom{\rule{3.33333pt}{0ex}}[0.738,\phantom{\rule{3.33333pt}{0ex}}0.938]$ | $0.831\phantom{\rule{3.33333pt}{0ex}}[0.716,\phantom{\rule{3.33333pt}{0ex}}0.947]$ | |

MIL-SVM | Dysplastic BE | $0.845\phantom{\rule{3.33333pt}{0ex}}[0.736,\phantom{\rule{3.33333pt}{0ex}}0.954]$ | $0.918\phantom{\rule{3.33333pt}{0ex}}[0.828,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.744\phantom{\rule{3.33333pt}{0ex}}[0.592,\phantom{\rule{3.33333pt}{0ex}}0.895]$ | $0.775\phantom{\rule{3.33333pt}{0ex}}[0.535,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.707\phantom{\rule{3.33333pt}{0ex}}[0.534,\phantom{\rule{3.33333pt}{0ex}}0.880]$ |

Non-dysplastic BE | $0.847\phantom{\rule{3.33333pt}{0ex}}[0.738,\phantom{\rule{3.33333pt}{0ex}}0.956]$ | $0.935\phantom{\rule{3.33333pt}{0ex}}[0.858,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.840\phantom{\rule{3.33333pt}{0ex}}[0.689,\phantom{\rule{3.33333pt}{0ex}}0.991]$ | $0.808\phantom{\rule{3.33333pt}{0ex}}[0.696,\phantom{\rule{3.33333pt}{0ex}}0.920]$ | $0.799\phantom{\rule{3.33333pt}{0ex}}[0.694,\phantom{\rule{3.33333pt}{0ex}}0.905]$ | |

Squamous | $0.989\phantom{\rule{3.33333pt}{0ex}}[0.978,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.998\phantom{\rule{3.33333pt}{0ex}}[0.995,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.983\phantom{\rule{3.33333pt}{0ex}}[0.946,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.968\phantom{\rule{3.33333pt}{0ex}}[0.931,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.974\phantom{\rule{3.33333pt}{0ex}}[0.949,\phantom{\rule{3.33333pt}{0ex}}0.999]$ | |

Weighted Average | $0.876\phantom{\rule{3.33333pt}{0ex}}[0.774,\phantom{\rule{3.33333pt}{0ex}}0.978]$ | $0.938\phantom{\rule{3.33333pt}{0ex}}[0.862,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.868\phantom{\rule{3.33333pt}{0ex}}[0.758,\phantom{\rule{3.33333pt}{0ex}}0.977]$ | $0.841\phantom{\rule{3.33333pt}{0ex}}[0.733,\phantom{\rule{3.33333pt}{0ex}}0.948]$ | $0.831\phantom{\rule{3.33333pt}{0ex}}[0.704,\phantom{\rule{3.33333pt}{0ex}}0.957]$ | |

EM-RF | Dysplastic BE | $0.837\phantom{\rule{3.33333pt}{0ex}}[0.719,\phantom{\rule{3.33333pt}{0ex}}0.954]$ | $0.896\phantom{\rule{3.33333pt}{0ex}}[0.783,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.668\phantom{\rule{3.33333pt}{0ex}}[0.436,\phantom{\rule{3.33333pt}{0ex}}0.901]$ | $0.747\phantom{\rule{3.33333pt}{0ex}}[0.480,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.676\phantom{\rule{3.33333pt}{0ex}}[0.439,\phantom{\rule{3.33333pt}{0ex}}0.912]$ |

Non-dysplastic BE | $0.836\phantom{\rule{3.33333pt}{0ex}}[0.717,\phantom{\rule{3.33333pt}{0ex}}0.954]$ | $0.915\phantom{\rule{3.33333pt}{0ex}}[0.818,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.809\phantom{\rule{3.33333pt}{0ex}}[0.649,\phantom{\rule{3.33333pt}{0ex}}0.969]$ | $0.817\phantom{\rule{3.33333pt}{0ex}}[0.676,\phantom{\rule{3.33333pt}{0ex}}0.958]$ | $0.794\phantom{\rule{3.33333pt}{0ex}}[0.661,\phantom{\rule{3.33333pt}{0ex}}0.928]$ | |

Squamous | $0.985\phantom{\rule{3.33333pt}{0ex}}[0.974,\phantom{\rule{3.33333pt}{0ex}}0.996]$ | $0.998\phantom{\rule{3.33333pt}{0ex}}[0.994,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.958\phantom{\rule{3.33333pt}{0ex}}[0.893,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.963\phantom{\rule{3.33333pt}{0ex}}[0.925,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.957\phantom{\rule{3.33333pt}{0ex}}[0.918,\phantom{\rule{3.33333pt}{0ex}}0.996]$ | |

Weighted Average | $0.865\phantom{\rule{3.33333pt}{0ex}}[0.760,\phantom{\rule{3.33333pt}{0ex}}0.971]$ | $0.923\phantom{\rule{3.33333pt}{0ex}}[0.825,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.828\phantom{\rule{3.33333pt}{0ex}}[0.670,\phantom{\rule{3.33333pt}{0ex}}0.986]$ | $0.829\phantom{\rule{3.33333pt}{0ex}}[0.713,\phantom{\rule{3.33333pt}{0ex}}0.944]$ | $0.814\phantom{\rule{3.33333pt}{0ex}}[0.671,\phantom{\rule{3.33333pt}{0ex}}0.958]$ | |

EM-SVM | Dysplastic BE | $0.858\phantom{\rule{3.33333pt}{0ex}}[0.738,\phantom{\rule{3.33333pt}{0ex}}0.979]$ | $0.900\phantom{\rule{3.33333pt}{0ex}}[0.805,\phantom{\rule{3.33333pt}{0ex}}0.994]$ | $0.757\phantom{\rule{3.33333pt}{0ex}}[0.524,\phantom{\rule{3.33333pt}{0ex}}0.990]$ | $0.757\phantom{\rule{3.33333pt}{0ex}}[0.486,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.709\phantom{\rule{3.33333pt}{0ex}}[0.474,\phantom{\rule{3.33333pt}{0ex}}0.945]$ |

Non-dysplastic BE | $0.859\phantom{\rule{3.33333pt}{0ex}}[0.739,\phantom{\rule{3.33333pt}{0ex}}0.980]$ | $0.938\phantom{\rule{3.33333pt}{0ex}}[0.870,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.846\phantom{\rule{3.33333pt}{0ex}}[0.688,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.863\phantom{\rule{3.33333pt}{0ex}}[0.733,\phantom{\rule{3.33333pt}{0ex}}0.994]$ | $0.834\phantom{\rule{3.33333pt}{0ex}}[0.705,\phantom{\rule{3.33333pt}{0ex}}0.963]$ | |

Squamous | $0.983\phantom{\rule{3.33333pt}{0ex}}[0.970,\phantom{\rule{3.33333pt}{0ex}}0.996]$ | $1.000\phantom{\rule{3.33333pt}{0ex}}[1.000,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.958\phantom{\rule{3.33333pt}{0ex}}[0.894,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.964\phantom{\rule{3.33333pt}{0ex}}[0.922,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.957\phantom{\rule{3.33333pt}{0ex}}[0.920,\phantom{\rule{3.33333pt}{0ex}}0.995]$ | |

Weighted Average | $0.883\phantom{\rule{3.33333pt}{0ex}}[0.775,\phantom{\rule{3.33333pt}{0ex}}0.991]$ | $0.935\phantom{\rule{3.33333pt}{0ex}}[0.861,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.852\phantom{\rule{3.33333pt}{0ex}}[0.691,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.850\phantom{\rule{3.33333pt}{0ex}}[0.732,\phantom{\rule{3.33333pt}{0ex}}0.969]$ | $0.834\phantom{\rule{3.33333pt}{0ex}}[0.687,\phantom{\rule{3.33333pt}{0ex}}0.981]$ | |

KM-RF | Dysplastic BE | $0.660\phantom{\rule{3.33333pt}{0ex}}[0.488,\phantom{\rule{3.33333pt}{0ex}}0.831]$ | $0.778\phantom{\rule{3.33333pt}{0ex}}[0.651,\phantom{\rule{3.33333pt}{0ex}}0.905]$ | $0.517\phantom{\rule{3.33333pt}{0ex}}[0.238,\phantom{\rule{3.33333pt}{0ex}}0.796]$ | $0.516\phantom{\rule{3.33333pt}{0ex}}[0.232,\phantom{\rule{3.33333pt}{0ex}}0.801]$ | $0.449\phantom{\rule{3.33333pt}{0ex}}[0.212,\phantom{\rule{3.33333pt}{0ex}}0.686]$ |

Non-dysplastic BE | $0.682\phantom{\rule{3.33333pt}{0ex}}[0.522,\phantom{\rule{3.33333pt}{0ex}}0.843]$ | $0.793\phantom{\rule{3.33333pt}{0ex}}[0.642,\phantom{\rule{3.33333pt}{0ex}}0.943]$ | $0.665\phantom{\rule{3.33333pt}{0ex}}[0.440,\phantom{\rule{3.33333pt}{0ex}}0.890]$ | $0.727\phantom{\rule{3.33333pt}{0ex}}[0.539,\phantom{\rule{3.33333pt}{0ex}}0.915]$ | $0.626\phantom{\rule{3.33333pt}{0ex}}[0.440,\phantom{\rule{3.33333pt}{0ex}}0.812]$ | |

Squamous | $0.954\phantom{\rule{3.33333pt}{0ex}}[0.910,\phantom{\rule{3.33333pt}{0ex}}0.998]$ | $0.996\phantom{\rule{3.33333pt}{0ex}}[0.990,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.877\phantom{\rule{3.33333pt}{0ex}}[0.725,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.976\phantom{\rule{3.33333pt}{0ex}}[0.946,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.907\phantom{\rule{3.33333pt}{0ex}}[0.806,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | |

Weighted Average | $0.720\phantom{\rule{3.33333pt}{0ex}}[0.575,\phantom{\rule{3.33333pt}{0ex}}0.865]$ | $0.836\phantom{\rule{3.33333pt}{0ex}}[0.715,\phantom{\rule{3.33333pt}{0ex}}0.957]$ | $0.728\phantom{\rule{3.33333pt}{0ex}}[0.547,\phantom{\rule{3.33333pt}{0ex}}0.908]$ | $0.648\phantom{\rule{3.33333pt}{0ex}}[0.484,\phantom{\rule{3.33333pt}{0ex}}0.812]$ | $0.631\phantom{\rule{3.33333pt}{0ex}}[0.453,\phantom{\rule{3.33333pt}{0ex}}0.810]$ | |

KM-SVM | Dysplastic BE | $0.676\phantom{\rule{3.33333pt}{0ex}}[0.542,\phantom{\rule{3.33333pt}{0ex}}0.809]$ | $0.754\phantom{\rule{3.33333pt}{0ex}}[0.642,\phantom{\rule{3.33333pt}{0ex}}0.867]$ | $0.512\phantom{\rule{3.33333pt}{0ex}}[0.322,\phantom{\rule{3.33333pt}{0ex}}0.702]$ | $0.632\phantom{\rule{3.33333pt}{0ex}}[0.412,\phantom{\rule{3.33333pt}{0ex}}0.852]$ | $0.509\phantom{\rule{3.33333pt}{0ex}}[0.353,\phantom{\rule{3.33333pt}{0ex}}0.662]$ |

Non-dysplastic BE | $0.705\phantom{\rule{3.33333pt}{0ex}}[0.565,\phantom{\rule{3.33333pt}{0ex}}0.845]$ | $0.776\phantom{\rule{3.33333pt}{0ex}}[0.641,\phantom{\rule{3.33333pt}{0ex}}0.912]$ | $0.672\phantom{\rule{3.33333pt}{0ex}}[0.468,\phantom{\rule{3.33333pt}{0ex}}0.876]$ | $0.643\phantom{\rule{3.33333pt}{0ex}}[0.499,\phantom{\rule{3.33333pt}{0ex}}0.788]$ | $0.615\phantom{\rule{3.33333pt}{0ex}}[0.461,\phantom{\rule{3.33333pt}{0ex}}0.769]$ | |

Squamous | $0.939\phantom{\rule{3.33333pt}{0ex}}[0.884,\phantom{\rule{3.33333pt}{0ex}}0.995]$ | $0.976\phantom{\rule{3.33333pt}{0ex}}[0.935,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.852\phantom{\rule{3.33333pt}{0ex}}[0.695,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.946\phantom{\rule{3.33333pt}{0ex}}[0.883,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.880\phantom{\rule{3.33333pt}{0ex}}[0.768,\phantom{\rule{3.33333pt}{0ex}}0.991]$ | |

Weighted Average | $0.733\phantom{\rule{3.33333pt}{0ex}}[0.608,\phantom{\rule{3.33333pt}{0ex}}0.857]$ | $0.812\phantom{\rule{3.33333pt}{0ex}}[0.695,\phantom{\rule{3.33333pt}{0ex}}0.929]$ | $0.743\phantom{\rule{3.33333pt}{0ex}}[0.597,\phantom{\rule{3.33333pt}{0ex}}0.889]$ | $0.660\phantom{\rule{3.33333pt}{0ex}}[0.531,\phantom{\rule{3.33333pt}{0ex}}0.790]$ | $0.664\phantom{\rule{3.33333pt}{0ex}}[0.527,\phantom{\rule{3.33333pt}{0ex}}0.802]$ | |

GMM-RF | Dysplastic BE | $0.948\phantom{\rule{3.33333pt}{0ex}}[0.907,\phantom{\rule{3.33333pt}{0ex}}0.989]$ | $0.985\phantom{\rule{3.33333pt}{0ex}}[0.967,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.921\phantom{\rule{3.33333pt}{0ex}}[0.834,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.929\phantom{\rule{3.33333pt}{0ex}}[0.843,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.914\phantom{\rule{3.33333pt}{0ex}}[0.856,\phantom{\rule{3.33333pt}{0ex}}0.972]$ |

Non-dysplastic BE | $0.941\phantom{\rule{3.33333pt}{0ex}}[0.903,\phantom{\rule{3.33333pt}{0ex}}0.979]$ | $0.983\phantom{\rule{3.33333pt}{0ex}}[0.965,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.892\phantom{\rule{3.33333pt}{0ex}}[0.776,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.947\phantom{\rule{3.33333pt}{0ex}}[0.912,\phantom{\rule{3.33333pt}{0ex}}0.982]$ | $0.910\phantom{\rule{3.33333pt}{0ex}}[0.840,\phantom{\rule{3.33333pt}{0ex}}0.981]$ | |

Squamous | $0.993\phantom{\rule{3.33333pt}{0ex}}[0.984,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.999\phantom{\rule{3.33333pt}{0ex}}[0.997,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.985\phantom{\rule{3.33333pt}{0ex}}[0.960,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.988\phantom{\rule{3.33333pt}{0ex}}[0.959,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.986\phantom{\rule{3.33333pt}{0ex}}[0.967,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | |

Weighted Average | $0.952\phantom{\rule{3.33333pt}{0ex}}[0.915,\phantom{\rule{3.33333pt}{0ex}}0.989]$ | $0.986\phantom{\rule{3.33333pt}{0ex}}[0.970,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.955\phantom{\rule{3.33333pt}{0ex}}[0.930,\phantom{\rule{3.33333pt}{0ex}}0.980]$ | $0.941\phantom{\rule{3.33333pt}{0ex}}[0.903,\phantom{\rule{3.33333pt}{0ex}}0.979]$ | $0.942\phantom{\rule{3.33333pt}{0ex}}[0.904,\phantom{\rule{3.33333pt}{0ex}}0.981]$ | |

GMM-SVM | Dysplastic BE | $0.937\phantom{\rule{3.33333pt}{0ex}}[0.913,\phantom{\rule{3.33333pt}{0ex}}0.961]$ | $0.988\phantom{\rule{3.33333pt}{0ex}}[0.980,\phantom{\rule{3.33333pt}{0ex}}0.997]$ | $0.814\phantom{\rule{3.33333pt}{0ex}}[0.708,\phantom{\rule{3.33333pt}{0ex}}0.921]$ | $0.976\phantom{\rule{3.33333pt}{0ex}}[0.948,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.879\phantom{\rule{3.33333pt}{0ex}}[0.811,\phantom{\rule{3.33333pt}{0ex}}0.946]$ |

Non-dysplastic BE | $0.931\phantom{\rule{3.33333pt}{0ex}}[0.909,\phantom{\rule{3.33333pt}{0ex}}0.954]$ | $0.973\phantom{\rule{3.33333pt}{0ex}}[0.959,\phantom{\rule{3.33333pt}{0ex}}0.987]$ | $0.937\phantom{\rule{3.33333pt}{0ex}}[0.882,\phantom{\rule{3.33333pt}{0ex}}0.991]$ | $0.862\phantom{\rule{3.33333pt}{0ex}}[0.814,\phantom{\rule{3.33333pt}{0ex}}0.910]$ | $0.895\phantom{\rule{3.33333pt}{0ex}}[0.858,\phantom{\rule{3.33333pt}{0ex}}0.933]$ | |

Squamous | $0.994\phantom{\rule{3.33333pt}{0ex}}[0.987,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $1.000\phantom{\rule{3.33333pt}{0ex}}[1.000,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $1.000\phantom{\rule{3.33333pt}{0ex}}[1.000,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.959\phantom{\rule{3.33333pt}{0ex}}[0.908,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.978\phantom{\rule{3.33333pt}{0ex}}[0.950,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | |

Weighted Average | $0.950\phantom{\rule{3.33333pt}{0ex}}[0.928,\phantom{\rule{3.33333pt}{0ex}}0.972]$ | $0.986\phantom{\rule{3.33333pt}{0ex}}[0.977,\phantom{\rule{3.33333pt}{0ex}}0.995]$ | $0.942\phantom{\rule{3.33333pt}{0ex}}[0.921,\phantom{\rule{3.33333pt}{0ex}}0.964]$ | $0.931\phantom{\rule{3.33333pt}{0ex}}[0.909,\phantom{\rule{3.33333pt}{0ex}}0.954]$ | $0.933\phantom{\rule{3.33333pt}{0ex}}[0.912,\phantom{\rule{3.33333pt}{0ex}}0.954]$ |

Number of Codeblock | KM | GMM | ||
---|---|---|---|---|

RF | SVM | RF | SVM | |

3 | $0.583\phantom{\rule{3.33333pt}{0ex}}[0.454,\phantom{\rule{3.33333pt}{0ex}}0.713]$ | $0.594\phantom{\rule{3.33333pt}{0ex}}[0.440,\phantom{\rule{3.33333pt}{0ex}}0.748]$ | $0.551\phantom{\rule{3.33333pt}{0ex}}[0.465,\phantom{\rule{3.33333pt}{0ex}}0.637]$ | $0.651\phantom{\rule{3.33333pt}{0ex}}[0.538,\phantom{\rule{3.33333pt}{0ex}}0.764]$ |

5 | $0.690\phantom{\rule{3.33333pt}{0ex}}[0.578,\phantom{\rule{3.33333pt}{0ex}}0.802]$ | $0.698\phantom{\rule{3.33333pt}{0ex}}[0.578,\phantom{\rule{3.33333pt}{0ex}}0.817]$ | $0.681\phantom{\rule{3.33333pt}{0ex}}[0.586,\phantom{\rule{3.33333pt}{0ex}}0.775]$ | $0.698\phantom{\rule{3.33333pt}{0ex}}[0.597,\phantom{\rule{3.33333pt}{0ex}}0.799]$ |

10 | $0.799\phantom{\rule{3.33333pt}{0ex}}[0.685,\phantom{\rule{3.33333pt}{0ex}}0.913]$ | $0.795\phantom{\rule{3.33333pt}{0ex}}[0.682,\phantom{\rule{3.33333pt}{0ex}}0.907]$ | $0.812\phantom{\rule{3.33333pt}{0ex}}[0.716,\phantom{\rule{3.33333pt}{0ex}}0.908]$ | $0.789\phantom{\rule{3.33333pt}{0ex}}[0.685,\phantom{\rule{3.33333pt}{0ex}}0.894]$ |

20 | $0.806\phantom{\rule{3.33333pt}{0ex}}[0.676,\phantom{\rule{3.33333pt}{0ex}}0.936]$ | $0.801\phantom{\rule{3.33333pt}{0ex}}[0.682,\phantom{\rule{3.33333pt}{0ex}}0.920]$ | $0.894\phantom{\rule{3.33333pt}{0ex}}[0.815,\phantom{\rule{3.33333pt}{0ex}}0.973]$ | $0.827\phantom{\rule{3.33333pt}{0ex}}[0.747,\phantom{\rule{3.33333pt}{0ex}}0.906]$ |

50 | $0.821\phantom{\rule{3.33333pt}{0ex}}[0.690,\phantom{\rule{3.33333pt}{0ex}}0.952]$ | $0.793\phantom{\rule{3.33333pt}{0ex}}[0.664,\phantom{\rule{3.33333pt}{0ex}}0.921]$ | $0.931\phantom{\rule{3.33333pt}{0ex}}[0.875,\phantom{\rule{3.33333pt}{0ex}}0.987]$ | $0.843\phantom{\rule{3.33333pt}{0ex}}[0.764,\phantom{\rule{3.33333pt}{0ex}}0.921]$ |

100 | $\mathbf{0}.\mathbf{836}[0.715,\phantom{\rule{3.33333pt}{0ex}}0.957]$ | $0.796\phantom{\rule{3.33333pt}{0ex}}[0.683,\phantom{\rule{3.33333pt}{0ex}}0.909]$ | $0.956\phantom{\rule{3.33333pt}{0ex}}[0.914,\phantom{\rule{3.33333pt}{0ex}}0.997]$ | $0.922\phantom{\rule{3.33333pt}{0ex}}[0.872,\phantom{\rule{3.33333pt}{0ex}}0.972]$ |

150 | $0.824\phantom{\rule{3.33333pt}{0ex}}[0.968,\phantom{\rule{3.33333pt}{0ex}}0.949]$ | $0.809\phantom{\rule{3.33333pt}{0ex}}[0.685,\phantom{\rule{3.33333pt}{0ex}}0.933]$ | $\mathbf{0}.\mathbf{986}[0.970,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $\mathbf{0}.\mathbf{986}[0.977,\phantom{\rule{3.33333pt}{0ex}}0.995]$ |

200 | $0.833\phantom{\rule{3.33333pt}{0ex}}[0.711,\phantom{\rule{3.33333pt}{0ex}}0.957]$ | $\mathbf{0}.\mathbf{812}[0.695,\phantom{\rule{3.33333pt}{0ex}}0.929]$ | $0.984\phantom{\rule{3.33333pt}{0ex}}[0.967,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.978\phantom{\rule{3.33333pt}{0ex}}[0.962,\phantom{\rule{3.33333pt}{0ex}}0.994]$ |

300 | $0.830\phantom{\rule{3.33333pt}{0ex}}[0.701,\phantom{\rule{3.33333pt}{0ex}}0.959]$ | $\mathbf{0}.\mathbf{812}[0.693,\phantom{\rule{3.33333pt}{0ex}}0.931]$ | $0.984\phantom{\rule{3.33333pt}{0ex}}[0.968,\phantom{\rule{3.33333pt}{0ex}}1.000]$ | $0.983\phantom{\rule{3.33333pt}{0ex}}[0.971,\phantom{\rule{3.33333pt}{0ex}}0.995]$ |

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Sali, R.; Moradinasab, N.; Guleria, S.; Ehsan, L.; Fernandes, P.; Shah, T.U.; Syed, S.; Brown, D.E.
Deep Learning for Whole-Slide Tissue Histopathology Classification: A Comparative Study in the Identification of Dysplastic and Non-Dysplastic Barrett’s Esophagus. *J. Pers. Med.* **2020**, *10*, 141.
https://doi.org/10.3390/jpm10040141

**AMA Style**

Sali R, Moradinasab N, Guleria S, Ehsan L, Fernandes P, Shah TU, Syed S, Brown DE.
Deep Learning for Whole-Slide Tissue Histopathology Classification: A Comparative Study in the Identification of Dysplastic and Non-Dysplastic Barrett’s Esophagus. *Journal of Personalized Medicine*. 2020; 10(4):141.
https://doi.org/10.3390/jpm10040141

**Chicago/Turabian Style**

Sali, Rasoul, Nazanin Moradinasab, Shan Guleria, Lubaina Ehsan, Philip Fernandes, Tilak U. Shah, Sana Syed, and Donald E. Brown.
2020. "Deep Learning for Whole-Slide Tissue Histopathology Classification: A Comparative Study in the Identification of Dysplastic and Non-Dysplastic Barrett’s Esophagus" *Journal of Personalized Medicine* 10, no. 4: 141.
https://doi.org/10.3390/jpm10040141