- Establishing a database By collecting ABUS and comprehensive breast image data, essential information, clinical treatment information, prognosis, and curative effect information, a complete breast image database is constructed.
- Marking ABUS image Three doctors use a semi-automatic method to frame the lesions on the image.
- Building the model Using the deep learning method to preprocess, analyze and train the marked image, and finally get a model diagnosis, efficacy evaluation and prognosis prediction model of breast cancer.
- Evaluating the model 1Self-validation Analyze the sensitivity, AUC of the breast cancer diagnosis model and the false-positive number on each ABUS volume.
- Compared the sensitivity, AUC and the false-positive number with a commercial diagnosis model.
3)By analyzing the size and characteristics of the lesions after neoadjuvant chemotherapy, and predicting the OS and DFS time, the therapy assessment and prognosis prediction model were evaluated.
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