RST2G: residual-guided spatiotemporal transformer graph fusion enhancement for breast cancer segmentation in DCE-MRI
Peer-Reviewed Publication
Updates every hour. Last Updated: 24-Jun-2026 05:16 ET (24-Jun-2026 09:16 GMT/UTC)
A research paper by scientists from Suining Central Hospital proposed a novel residual-guided spatiotemporal transformer with graph fusion enhancement (RST2G) framework for precise breast tumor segmentation in ynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).
The new research paper, published on Mar. 23 in the journal Cyborg and Bionic Systems, developed a groundbreaking deep learning framework for precise breast tumor segmentation in DCE-MRI.
A new study by researchers at Oxford Population Health and in China, using data from the China Kadoorie Biobank (CKB), has shown that entering adulthood with a healthy body weight is associated with a substantially lower risk of premature death from cardiovascular disease, cancer and respiratory disease. These associations were shown to be independent of body weight later in life, indicating that excess weight in early adulthood has lasting effects that are not fully reversed by subsequent weight change.
A study led by Qun-Ying Lei shows that PGG inhibits the MAT2A enzyme activity while simultaneously promoting its degradation. This unique dual-action induces pyroptosis and enhances antitumor immunity, presenting a promising new strategy for cancer immunotherapy.