Tumor detection on bronchoscopic images by unsupervised learning.

The diagnosis and early identification of intratracheal tumors relies on the experience of the operators and the specialists. Operations by physicians with insufficient experience may lead to misdiagnosis or misjudgment of tumors. To address this issue, a datasets for intratracheal tumor detection has been constructed to simulate the diagnostic level of experienced specialists, and a Knowledge Distillation-based Memory Feature Unsupervised Anomaly Detection (KD-MFAD) model was proposed to learn from this simulated experience. The unsupervised training approach could effectively deal with the irregular features of the tumorous appearance. The Downward Deformable Convolution Module (DDC) allowed the encoding phase to provide more detailed internal airway environment features. The Memory Matrix based on Convolutional Block focusing (CB-Mem) helped the student model store more meaningful normal sample features during training and disrupted the reconstruction of "tumor" images. Our model achieved an AUC-ROC of 97.60%, Acc of 93.33%, and F1-score of 94.94% on our self-built intratracheal endoscopy datasets, improving baseline performance by 5 to 10%. Our model also demonstrated superior performance over existing models in the public datasets in the same field.