Histology for Pathologists

Anatomic pathology instruments and reagents
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Practice of Pathology

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By specifying your reason for contact we will be able to provide you with a better service. Follow up information. Preferred method of contact. Preferred method of contact Work Email Phone. Best time to call. Best time to call Early morning Late morning Early afternoon Late afternoon. Estimated time of purchase. For the training set, pathologists annotated 4, crops from images, about 17 crops per image.

Our benign class also included inflammation, scarring, fibrosis, and artifacts. Since these patches are used for model selection and development, all labels in this set were independently verified by two pathologists, and patches with disagreements were discarded. Our test set is whole-slide images, each of which contains one or more of the five histological patterns. Our three pathologists independently labeled all images on the whole-slide level, specifying the predominant and minor patterns.

After our model development was completed, we evaluated our model on this test set and compared its performance to those of our pathologist annotators. Deep learning models, such as convolutional neural networks, have been increasingly applied to computer vision and medical image analysis due to breakthroughs in high-performance computing and the availability of large datasets. In our study, we leverage the deep residual network ResNet 37 , a type of convolutional neural network that uses residual blocks to achieve state-of-the-art performance on image recognition benchmarks such as ImageNet 38 and COCO We implemented ResNet to take in square patches as inputs and output a prediction probability for each of the five histological patterns and benign tissue, six classes in total.

We trained our model on 4, annotated crops from the training set.

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Because each of these crops is of variable size, we used a sliding window algorithm to generate multiple smaller patches of fixed length and width from each crop. Some classes contained more crops than others, so we generated patches with different overlapping areas for each class to form a uniform class distribution. Before inputting a patch into the model for training, we normalized the color channel values to the mean and standard deviation of the entire training set to neutralize color differences between slides.

Next, we augmented our training set by performing color jittering on the brightness, contrast, saturation, and hue of each image. Our final training set consisted of approximately eight thousand patches per class. As for model parameters, we initialized the network weights with the He initialization We trained for fifty epochs on the augmented training set, starting with an initial learning rate of 0.

Our model used the multi-class cross-entropy loss function.

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To find the optimal depth for the residual network, we conducted an ablation test on ResNets of 18, 34, 50, , and layers. We found they all obtain similar accuracies on our development set, so we chose ResNet since it has the smallest number of parameters and the fastest training time. At inference time, we aimed to detect all predominant and minor patterns at the whole-slide level.

The Pathologist

But because our trained ResNet classifies patches, not entire slides, we first broke down each whole slide into a collection of patches by sliding a fixed-size window over the entire image. We then classified each patch and filtered out noise by using thresholding to discard predictions of low confidence.

Thresholds are determined by a grid search over each pattern class, optimizing for the correspondence between our model and whole-slide labels on the development set. Considering the distribution of the predicted patch patterns for each slide, we then used a three-step heuristic to classify the whole slide. First, classes comprising less than five percent of the patch predictions, as well as the benign class, were dropped.

Then, the most frequent class was assigned to the predominant label.


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Finally, all remaining cancerous pattern classes were assigned to minor labels. By discarding predictions of low confidence and aggregating over a large number of patches, our model is robust to artifacts from tissue staining, as well as single-patch misclassifications. A schematic overview of the whole-slide inference process is shown in Fig. Evaluation time of our model for a single whole slide was around thirty seconds.

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For final evaluation, we ran our model on the test set of whole-slide images. We also asked our three pathologists to independently label the predominant and minor patterns in all whole-slide images. As a result, we had four sets of whole-slide classifications in total: three from pathologists, and one from our model. First, because histologic patterns are only determined from subjective reviews by pathologists, no ground truth labels exist to calculate F1-scores or AUC.

Second, previous studies on classifying histologic patterns use kappa score as a standard metric 19 , 20 , so we follow this convention to facilitate comparison between our results and those of previous literature. Predominant agreement is the percentage of whole slides in which two annotators agreed on the predominant pattern.