FBN: Weakly Supervised Thyroid Nodule Segmentation Optimized by Online Foreground and Background.

Objective: The main objective of the work described here was to train a semantic segmentation model using classification data for thyroid nodule ultrasound images to reduce the pressure of obtaining pixel-level labeled data sets. Furthermore, we improved the segmentation performance of the model by mining the image information to narrow the gap between weakly supervised semantic segmentation (WSSS) and fully supervised semantic segmentation.

Methods: Most WSSS methods use a class activation map (CAM) to generate segmentation results.

ATTransUNet: An enhanced hybrid transformer architecture for ultrasound and histopathology image segmentation.

Recently, researchers have introduced Transformer into medical image segmentation networks to encode long-range dependency, which makes up for the deficiencies of convolutional neural networks (CNNs) in global context modeling, and thus improves segmentation performance. However, in Transformer, due to the heavy computational burden of paired attention modeling between redundant visual tokens, the efficiency of Transformer needs to be further improved. Therefore, in this paper, we propose ATTransUNet, a Transformer enhanced hybrid architecture based on the adaptive token for ultrasound and histopathology image segmentation.