We are thrilled to announce that our team has published five significant papers at the Radiological Society of North America (RSNA). These publications highlight breakthroughs in breast cancer treatment prediction, personalized therapy recommendations, and survival analysis.
Key Achievements:
UNCERTAINTY-AWARE COLLABORATIVE LEARNING BETWEEN AI AND RADIOLOGISTS FOR PREDICTING NEOADJUVANT THERAPY RESPONSE IN BREAST CANCER
GLOBAL-LOCAL LEARNING FOR EXPLAINABLE BREAST CANCER RISK PREDICTION FROM SCREENING MAMMOGRAMS
PERSONALIZED NEOADJUVANT THERAPY RECOMMENDATIONS IN BREAST CANCER FROM A MULTI-OMICS CAUSAL ARTIFICIAL INTELLIGENCE RESPONSE MODEL
END-TO-END PROGNOSTICATION AND SURVIVAL ANALYSIS OF BREAST CANCER BY COMBINED VISUAL-LANGUAGE DEEP LEARNING
PREDICTING FIVE-YEAR POST-TREATMENT BREAST CANCER RECURRENCE USING MULTI-TIME-POINT MAMMOGRAMS AND MEDICAL REPORTS
A MULTIMODAL DEEP LEARNING MODEL BASED ON PET/CT IMAGES AND ASSOCIATED REPORTS FOR BREAST CANCER SURVIVAL PREDICTION AND ANALYSIS