CT imaging 1deep learning 1distant metastasis prediction 1foundation models 1head and neck cancer 1radiomics 1
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cs.CV2026
Comparing the Performance of Foundation Model Derived Embeddings with Traditional Approaches for Distant Metastasis Prediction in Head and Neck Cancer
Erich Schmitz, Meixu Chen, Bowen Jing +1
The study evaluates CT foundation model embeddings for predicting distant metastasis in head and neck cancer and finds they outperform traditional radiomics and deep‑learning featu…
cs.CV2025
A two-stage dual-task learning strategy for early prediction of pathological complete response to neoadjuvant chemotherapy for breast cancer using dynamic contrast-enhanced magnetic resonance images
Bowen Jing, Jing Wang
Rationale and Objectives: Early prediction of pathological complete response (pCR) can facilitate personalized treatment for breast cancer patients. To improve prediction accuracy…
cs.CV2024
nnSAM: Plug-and-play Segment Anything Model Improves nnUNet Performance
Yunxiang Li, Bowen Jing, Zihan Li +2
Automatic segmentation of medical images is crucial in modern clinical workflows. The Segment Anything Model (SAM) has emerged as a versatile tool for image segmentation without sp…