Self-Supervised Learning For Detection Of Breast Cancer In Surgical Margins With Limited Data

Breast conserving surgery is a standard cancer treatment to resect breast tumors while preserving healthy tissue. The reoperation rate can be as high as 35% due to the difficulties associated with detection of remaining cancer in surgical margins. REIMS is a mass spectrometry method that can address this challenge through real-time measurement of molecular signature of tissue. However, the collection of breast spectra to train a cancer detection model is time consuming and large samples sizes are not practical. We propose an application of self-supervised learning to improve the performance of cancer detection at surgical margins using a limited number of labelled data samples. A deep model is trained for the intermediate task of capturing latent features of REIMS data without the use of cancer labels. The model compensates for the small data size by dividing the spectra into smaller patches and shuffling their order, generating new instances. By interrogating the shuffled data and learning the order of its patches, the model captures the characteristics of the data. The learnt weights from the model are then transferred to a subsequent network and fine-tuned for cancer detection. The proposed method achieved the accuracy, sensitivity and specificity to 97%, 91% and 100%, respectively, in data from 144 cancer and normal REIMS samples.