23 abstracts accepted for RSNA 2026
Bagci Lab members and collaborators have 23 abstracts accepted for RSNA 2026. Dr. Bagci will also deliver lectures and tutorials on generative AI and language models.
Dr. Bagci and his lab members, collaborators have 23 abstracts accepted to RSNA 2026! This is a remarkable progress by an AI lab. Dr. Bagci will also deliver lectures/tutorials about genAI / LLM / VLM at RSNA 2026.
Here are the 23 abstracts brief list.
TAGS – FOUNDATIONAL TUMOR SEGMENTATION MODEL
Ertugrul Aktas et al
SELF-CORRECTING AI DETECTS 50% MORE SMALL PANCREATIC CANCERS THAN RADIOLOGISTS ON ROUTINE CONTRAST-ENHANCED CT ACROSS 147 HOSPITALS WORLDWIDE
Zongwei Zhou et al
SEEING MORE, DWELLING LESS: BIAS-FIELD CORRECTION SYSTEMATICALLY ALTERS RADIOLOGIST VISUAL SEARCH BEHAVIOR DURING MRI INTERPRETATION
Ertugrul Aktas et al
SCALEMAI: AN EXPECTATION-MAXIMIZATION ENGINE THAT CO-EVOLVES AI AND ANNOTATIONS TO BUILD A 47,000-SCAN PANCREATIC CT DATASET
Zongwei Zhou et al
REPORT-TRAINED SEGMENTATION AI DETECTS 18% MORE TUMORS THAN RADIOLOGISTS ACROSS 7 HARD-TO-SEE CANCERS ON ROUTINE CT
Zongwei Zhou et al
RADIOLOGY INSPIRED PEDIATRIC BRAIN TUMOR SEGMENTATION FOR RELIABLE MULTICENTER DEPLOYMENT
Elif Keles et al
PROGNOSTIC VALUE OF FUNCTIONAL LIVER IMAGING SCORE AND LIVER SURFACE NODULARITY ON GADOXETIC ACID-ENHANCED MRI IN CIRRHOSIS
Yavuz Bahadir Taktak et al
PERFORMANCE OF VISION LARGE LANGUAGE MODELS COMPARED WITH RADIOLOGY EXPERTS IN THE ASSESSMENT OF PEDIATRIC SPINE RADIOGRAPHS
Mucahit Ekici et al
MULTISCALE MRI RADIOMICS IMPROVES MOLECULARLY INFORMED RISK STRATIFICATION IN HIGH-GRADE GLIOMA
Yury Velichko et al
MULTIMODAL AI FOR EARLY PREDICTION OF ADVERSE CLINICAL OUTCOMES IN ACUTE PANCREATITIS
Ertugrul Aktas et al.
MRI-BASED AI MODEL FOR PREOPERATIVE STAGING OF LARYNGEAL SQUAMOUS CELL CARCINOMA
Fergan Bol et al.
MERLIN PLUS: FIRST PUBLIC CT TUMOR-MASK-REPORT DATASET FOR NINE CANCERS CURRENTLY LACKING SEGMENTATION ANNOTATIONS
Zongwei Zhou et al
LUMINA: A MULTI-VENDOR MAMMOGRAPHY BENCHMARK WITH ENERGY HARMONIZATION PROTOCOL
Ertugrul Aktas et al.
LOOKING BEYOND THE LESION: COMBINING TUMOR, PANCREAS, DUCT, AND CLINICAL BIOMARKERS DETECTS EARLY PANCREATIC CANCER ON ROUTINE CT ACROSS 147 CENTERS
Zongwei Zhou et al
LONG-TERM RISK OF ADVANCED PANCREAS NEOPLASIA IN PATIENTS WITH PANCREAS CYSTIC NEOPLASMS: RESULTS FROM A LARGE MULTI-CENTER COHORT STUDY
Andrea Bejar et al
LARGE LANGUAGE MODELS VERSUS RADIOLOGY EXPERTS IN ACR-BASED ABDOMINAL MRI PROTOCOL SELECTION
Mucahit Ekici et al
FEASIBILITY OF A STATE-OF-THE-ART AUTOMATED PANCREAS SEGMENTATION ALGORITHM IN HETEROGENEOUS MULTIMODAL IMAGING COHORTS
Eminenur Sen Tasci et al
DEEP LEARNING-BASED AUTOMATED PI-QUAL ASSESSMENT OF PROSTATE BIPARAMETRIC MRI
Enes Tasci et al
AUGMENTED AND VIRTUAL REALITY FOR PREPROCEDURAL PLANNING IN TRANSARTERIAL INTERVENTIONAL ONCOLOGY: A PILOT FEASIBILITY AND USABILITY STUDY
Saad Abu Zahra et al
ARTIFICIAL INTELLIGENCE FOR PROSTATE SEGMENTATION ON MRI IN GLOBAL POPULATIONS
Tiago Coelho et al
ARTIFICIAL INTELLIGENCE DETECTS PANCREATIC CANCER ON CT NEARLY ONE YEAR BEFORE CLINICAL DIAGNOSIS ACROSS THREE INTERNATIONAL CENTERS
Zongwei Zhou et al
AI TRANSFORMATION IN CIRRHOSIS MANAGEMENT
Enes Tasci et al.
AI FOR PREDICTING RECURRENCE AND LOCAL TUMOR PROGRESSION AFTER LOCOREGIONAL THERAPIES IN HEPATOCELLULAR CARCINOMA ON MRI
Mehmet Akpinar et al.
See you at RSNA 2026, December first week!
