Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images (1705.03678v1)

Abstract: Automated classification of histopathological whole-slide images (WSI) of breast tissue requires analysis at very high resolutions with a large contextual area. In this paper, we present context-aware stacked convolutional neural networks (CNN) for classification of breast WSIs into normal/benign, ductal carcinoma in situ (DCIS), and invasive ductal carcinoma (IDC). We first train a CNN using high pixel resolution patches to capture cellular level information. The feature responses generated by this model are then fed as input to a second CNN, stacked on top of the first. Training of this stacked architecture with large input patches enables learning of fine-grained (cellular) details and global interdependence of tissue structures. Our system is trained and evaluated on a dataset containing 221 WSIs of H&E stained breast tissue specimens. The system achieves an AUC of 0.962 for the binary classification of non-malignant and malignant slides and obtains a three class accuracy of 81.3% for classification of WSIs into normal/benign, DCIS, and IDC, demonstrating its potentials for routine diagnostics.

• The paper proposes a two-tiered stacked CNN architecture that analyzes both high-resolution cellular details and broader spatial tissue relationships in whole-slide images.
• Using a dataset of 221 WSIs, the model achieved an AUC of 0.962 for binary classification (non-malignant/malignant) and 81.3% accuracy for classifying normal/benign, DCIS, and IDC.
• This context-aware CNN approach holds promise for improving diagnostic accuracy in histopathology and could be applied to other imaging modalities requiring fine-grained classification for better diagnostics.

Classification of Breast Carcinomas Using Context-Aware Stacked Convolutional Neural Networks

The paper "Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images" presents a methodical exploration into the use of deep learning techniques for the classification of breast tissue in histopathology images. The focus of the paper is on leveraging context-aware stacked convolutional neural networks (CNNs) to distinguish between normal/benign tissue, ductal carcinoma in situ (DCIS), and invasive ductal carcinoma (IDC).

The research discusses the challenges associated with the classification of whole-slide images (WSIs) due to their inherently high resolution and the need to evaluate large contextual areas accurately. The proposed method introduces a two-tiered CNN architecture. Initially, a CNN is trained using high-resolution image patches to capture detailed cellular information. The generated feature maps from this network serve as the input to a second CNN. This two-level approach enables the system to learn both fine-grained cellular details and the broader spatial interrelationships of tissue architectures simultaneously.

The paper utilizes a dataset comprising 221 WSIs of Hematoxylin and Eosin (H&E) stained breast tissue samples. The training and evaluation metrics reveal an area under the curve (AUC) of 0.962 for the binary classification task, distinguishing non-malignant from malignant slides. The model also reported an accuracy of 81.3% when classifying tissue into one of three categories: normal/benign, DCIS, or IDC. These metrics indicate considerable promise for the application of the system in routine diagnostic settings.

The implications of this paper are multifaceted. Practically, the advancement of context-aware CNNs holds substantial promise for improving diagnostic accuracy in histopathological practice, potentially easing the workload of pathologists by providing reliable preliminary assessments. Theoretically, the architecture paves the way for further research into context-aware deep learning networks across various imaging modalities beyond histopathology, potentially impacting fields that require fine-grained image classification.

While the results are encouraging, there is potential for further research, particularly in enhancing model training with larger and more diverse datasets to improve generalizability. Moreover, the integration of this technology into clinical workflows will necessitate rigorous validation in prospective settings and assessments of the system's interpretability to ensure alignment with clinical needs.

In conclusion, this research adds to the growing body of literature emphasizing the potential of sophisticated CNN architectures in transforming histopathological analysis. Future directions could include refining the technology to manage more complex classification tasks and further exploring its applicability in other pathological conditions.