Breast Ultrasound Tumor Detection Based on Active Learning and Deep Learning

EasyChair Preprint no. 6650

Abstract

Early breast cancer screening and diagnosis policy plays a significance role in reducing breast cancer mortality, which is the most common malignant tumor for women. Therefore, its accuracy and efficiency are very important. To cover these challenges in mass breast screening and diagnosis, including varied ultrasound image quality from different equipments, expensive professional annotation, we propose a novel method based on active learning and convolution neural networks for selecting more informative images and tumor detection, respectively. Firstly, we verify the effectiveness of active learning in the application of our breast ultrasound data. Secondly, we select the informative images from the origin training set using the Multiple Instance Active Learning (MIAL) with One-Shot Path Aggregation Feature Pyramid Network (OPA-FPN) structure. Through this way, we effectively balance the ratio of hard samples and simple samples in the origin training set. Finally, we train the model based on EfficientDet with specific and valid parameters for our breast ultrasound data. Through the corresponding ablation experiment, it is verified that the model trained on the selected dataset by combining MIAL with OPA_FPN exceeds the origin model in the metrics about sensitive, specificity and F1-score. Meanwhile, while keeping the corresponding metrics approximately the same, the confidence of inference images from the new model is higher and stable.

Keyphrases: active learning, Breast cancer screening and diagnosis, Breast ultrasound, Tumor Detection

@Booklet{EasyChair:6650,
  author = {Gen Liu and Jiayi Wu and Hongguang Yang and Yuanwei Li and Xi Sun and Jiyong Tan and Baoming Luo},
  title = {Breast Ultrasound Tumor Detection Based on Active Learning and Deep Learning},
  howpublished = {EasyChair Preprint no. 6650},

  year = {EasyChair, 2021}}