Deep learning classification of deep ultraviolet fluorescence images toward intra-operative margin assessment in breast cancer.

Background: Breast-conserving surgery is aimed at removing all cancerous cells while minimizing the loss of healthy tissue. To ensure a balance between complete resection of cancer and preservation of healthy tissue, it is necessary to assess themargins of the removed specimen during the operation. Deep ultraviolet (DUV) fluorescence scanning microscopy provides rapid whole-surface imaging (WSI) of resected tissues with significant contrast between malignant and normal/benign tissue. Intra-operative margin assessment with DUV images would benefit from an automated breast cancer classification method. Methods: Deep learning has shown promising results in breast cancer classification, but the limited DUV image dataset presents the challenge of overfitting to train a robust network. To overcome this challenge, the DUV-WSI images are split into small patches, and features are extracted using a pre-trained convolutional neural network-afterward, a gradient-boosting tree trains on these features for patch-level classification. An ensemble learning approach merges patch-level classification results and regional importance to determine the margin status. An explainable artificial intelligence method calculates the regional importance values. Results: The proposed method's ability to determine the DUV WSI was high with 95% accuracy. The 100% sensitivity shows that the method can detect malignant cases efficiently. The method could also accurately localize areas that contain malignant or normal/benign tissue. Conclusion: The proposed method outperforms the standard deep learning classification methods on the DUV breast surgical samples. The results suggest that it can be used to improve classification performance and identify cancerous regions more effectively.

Deep Learning for Breast Cancer Classification of Deep Ultraviolet Fluorescence Images toward Intra-Operative Margin Assessment.

Breast conserving surgery aims at the complete removal of malignant lesions while minimizing healthy tissue loss. To ensure the balance between complete resection of the cancer and conservation of healthy tissue, intra-operative margin assessment is necessary. Deep ultraviolet (DUV) fluorescence scanning microscope provides fast whole-surface-imaging (WSI) of excised tissue with contrast between malignant and normal tissues. Then, an automated breast cancer classification method on DUV images is required for intra-operative margin assessment. Deep learning shows the promising results in breast cancer classification, but limited DUV image dataset poses overfitting challenge to train the robust network. To tackle this challenge, we partition the DUV WSI image into small patches and extract pathological features for each patch from a pre-trained network using a transfer learning approach. We feed pathological features into a decision-tree-based classifier and fuse patch-level classification results based on regional importance to determine malignant or benign WSI. Experimental results on 60 DUV images show that our proposed method outperforms the standard deep learning classification in terms of improving the classification performance and identifying cancerous regions.