Automated assessment of breast cancer margin in optical coherence tomography images via pretrained convolutional neural network

Abstract

The benchmark method for the evaluation of breast cancers involves microscopic testing of a hematoxylin and eosin (H&E)‐stained tissue biopsy. Resurgery is required in 20% to 30% of cases because of incomplete excision of malignant tissues. Therefore, a more accurate method is required to detect the cancer margin to avoid the risk of recurrence. In the recent years, convolutional neural networks (CNNs) has achieved excellent performance in the field of medical images diagnosis. It automatically extracts the features from the images and classifies them. In the proposed study, we apply a pretrained Inception‐v3 CNN with reverse active learning for the classification of healthy and malignancy breast tissue using optical coherence tomography (OCT) images. This proposed method attained the sensitivity, specificity and accuracy is 90.2%, 91.7% and 90%, respectively, with testing datasets collected from 48 patients (22 normal fibro‐adipose tissue and 26 Invasive ductal carcinomas cancerous tissues). The trained network utilizes for the breast cancer margin assessment to predict the tumor with negative margins. Additionally, the network output is correlated with the corresponding histology image. Our results lay the foundation for the future that the proposed method can be used to perform automatic intraoperative identification of breast cancer margins in real‐time and to guide core needle biopsies.

Deep learning a boon for biophotonics?

“…This technique focuses on transferring features of a deep neural network learned on a larger dataset to a small dataset. Research has shown that transfer‐learning strategies lead to promising results when applied for small spectroscopic dataset . However, transferring features of a deep neural network which is pretrained on a dataset like ImageNet, to perform classification or regression tasks on spectroscopic data, is debatable.…”

“…Based on the two transfer learning strategies, the size of the dataset, the similarity between the datasets and the similarity between the tasks (classification or regression) involved, four major approaches can be utilized : If the new dataset is small and similar to the original dataset, then the generic features from the top layers of a pretrained deep neural network will be relevant for the new dataset and thus these generic features can be used to train an easy classifier. If the new dataset is large and similar to the original dataset, then fine‐tuning of the whole pretrained deep neural network can be performed. If the new dataset is small and different from the original dataset, then it is best to train a linear classifier (linear discriminant analysis or support vector machine) by using activations from the top and intermediate layers of a pretrained deep neural network. Previous research reported that this method works best for small spectroscopic datasets . However, for biophotonics this needs proper investigation depending on the dataset. If the new dataset is large and different from the original dataset, then it is beneficial to train a deep neural network from scratch and initialize the weights using a similar pretrained deep neural network model. …”

“…Therefore, the training of MLPs or CNNs is always challenging due to the small datasets available. In such cases, deep learning networks using transfer learning strategies can be applied . Transfer learning utilizes CNN models pretrained on a (large) source dataset and transfer the learned features to classify a (small) target dataset.…”

“…Transfer learning utilizes CNN models pretrained on a (large) source dataset and transfer the learned features to classify a (small) target dataset. For example, pretrained CNN models including GoogleNet , Inceptionv3 and VGG16 were used to classify breast cancer in OCT images , head and neck cancer in 3‐D OCT images , lung cancer in CARS images and hepatocellular carcinoma in multiphoton microscopic images (see Figure top), respectively. Here, the CNN models are first trained on large nonbiological datasets like ImageNet and the parameters of these pretrained models are fine‐tuned on the new biophotonic dataset.…”

“…To summarize, MLPs and CNNs are majorly used for disease classification. Recently reported transfer learning strategies using CNNs have worked best for small datasets, which are accounted in biophotonic studies.…”