MRI Breast Tumor Segmentation Using Different Encoder and Decoder Using CNN Architecture

Detecting an anomaly such as a malignant tumor or a nodule from medical images including mammogram, CT, or PET images is still an ongoing research problem drawing a lot of attention with applications in medical diagnosis. The learned model can be used to classify a testing sample into a positive or negative class.

However, in medical applications, the high unbalance between negative and positive samples pose a difficulty for learning algorithms, as they will be biased towards the majority group, i.e., the negative one. To address this imbalanced data issue as well as leverage the huge amount, of negative samples, i.e., normal mammogram images, we propose to learn an unsupervised model to characterize the negative class.

To make the learned model more flexible and extendable for medical images of different scales, we have designed an autoencoder based on a deep neural network to characterize the negative patches decomposed from large breast cancer images.