Our team published PCsRNAdb in Nucleic Acids Research, a comprehensive pan-cancer resource for studying small noncoding RNAs across tumor types. The database was built to support …
Our team introduced CRCFound in Advanced Science, a self-supervised colorectal cancer CT foundation model pretrained on large-scale unlabeled 3D CT data. CRCFound was …
Our team published SegMamba-V2 in IEEE Transactions on Medical Imaging, a general framework for long-range modeling in 3D medical image segmentation. The model extends Mamba-based …
Our team published PIANOS in Nature Communications to address a long-standing challenge in colorectal cancer prognosis: model instability across platforms and cohorts. PIANOS is a …
Our team reported Brim in Advanced Science, an interpretable multimodal fusion model for bridged histology-genomics survival prediction in pan-cancer. Brim is trained with both …
Our team published a review in Genomics Proteomics Bioinformatics on the opportunities and challenges of AI-driven biomedical multimodal data fusion. The review summarizes how deep …
Our team reported in International Journal of Surgery that tertiary lymphoid structure heterogeneity can define clinically meaningful colorectal cancer subgroups. The study …
Our team published TMO-Net in Genome Biology, an explainable pretrained multi-omics model designed for multi-task learning in oncology. The work focuses on learning transferable …
Our team reported a MedComm study showing that senescence-related molecular programs can stratify colorectal cancer into clinically meaningful subtypes. By integrating …
Our team introduced SOUSA in Medical Image Analysis, a unified weakly and semi-supervised framework for medical image segmentation using sparse annotations. SOUSA is designed for …