BTS-ST: Swin transformer network for segmentation and classification of multimodality breast cancer images
Breast cancer is considered the most commonly diagnosed cancer globally and falls second
to lung cancer. For the early detection of breast tumors in women, breast cancer analysis …
to lung cancer. For the early detection of breast tumors in women, breast cancer analysis …
Boosting semantic segmentation from the perspective of explicit class embeddings
Semantic segmentation is a computer vision task that associates a label with each pixel in
an image. Modern approaches tend to introduce class embeddings into semantic …
an image. Modern approaches tend to introduce class embeddings into semantic …
Car: Class-aware regularizations for semantic segmentation
Recent segmentation methods, such as OCR and CPNet, utilizing “class level” information in
addition to pixel features, have achieved notable success for boosting the accuracy of …
addition to pixel features, have achieved notable success for boosting the accuracy of …
Attention-based neural network for polarimetric image denoising
In this Letter, we propose an attention-based neural network specially designed for the
challenging task of polarimetric image denoising. In particular, the channel attention …
challenging task of polarimetric image denoising. In particular, the channel attention …
Temporally-Consistent Video Semantic Segmentation with Bidirectional Occlusion-guided Feature Propagation
RK Baghbaderani, Y Li, S Wang… - Proceedings of the IEEE …, 2024 - openaccess.thecvf.com
Despite recent progress in static image segmentation, video segmentation is still
challenging due to the need for an accurate, fast, and temporally consistent model …
challenging due to the need for an accurate, fast, and temporally consistent model …
Class semantic enhancement network for semantic segmentation
Existing semantic segmentation methods favor class semantic consistency by extracting
long-range contextual features through multi-scale and attention strategies. These methods …
long-range contextual features through multi-scale and attention strategies. These methods …
Modification of a conventional deep learning model to classify simulated breathing patterns: a step toward real-time monitoring of patients with respiratory infectious …
The emergence of the global coronavirus pandemic in 2019 (COVID-19 disease) created a
need for remote methods to detect and continuously monitor patients with infectious …
need for remote methods to detect and continuously monitor patients with infectious …
Representation separation for semantic segmentation with vision transformers
Vision transformers (ViTs) encoding an image as a sequence of patches bring new
paradigms for semantic segmentation. We present an efficient framework of representation …
paradigms for semantic segmentation. We present an efficient framework of representation …
Multi-query and multi-level enhanced network for semantic segmentation
Plain transformer-based methods have achieved promising performance on semantic
segmentation recently. These methods adopt a single set of class queries to predict masks …
segmentation recently. These methods adopt a single set of class queries to predict masks …
Remote sensing image semantic segmentation via class-guided structural interaction and boundary perception
X He, Y Zhou, B Liu, J Zhao, R Yao - Expert Systems with Applications, 2024 - Elsevier
Existing remote sensing semantic segmentation methods generally ignore the structural
information of objects that is vital in the human visual recognition system. The absence of …
information of objects that is vital in the human visual recognition system. The absence of …