Towards reliable healthcare Imaging: conditional contrastive generative adversarial network for handling class imbalancing in MR Images.

ABSTRACT:

Background

Medical imaging datasets frequently encounter a data imbalance issue, where the majority of pixels correspond to healthy regions, and the minority belong to affected regions. This uneven distribution of pixels exacerbates the challenges associated with computer-aided diagnosis. The networks trained with imbalanced data tends to exhibit bias toward majority classes, often demonstrate high precision but low sensitivity.

Method

We have designed a new network based on adversarial learning namely conditional contrastive generative adversarial network (CCGAN) to tackle the problem of class imbalancing in a highly imbalancing MRI dataset. The proposed model has three new components: (1) class-specific attention, (2) region rebalancing module (RRM) and supervised contrast

SUBMITTER: Cui L 

PROVIDER: S-EPMC11323102 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature