4 papers
DiA-gnostic VLVAE: Disentangled Alignment-Constrained Vision Language Variational AutoEncoder for Robust Radiology Reporting with Missing Modalities
Nagur Shareef Shaik, Teja Krishna Cherukuri, Adnan Masood +1
The integration of medical images with clinical context is essential for generating accurate and clinically interpretable radiology reports. However, current automated methods ofte…
Ordinal Label-Distribution Learning with Constrained Asymmetric Priors for Imbalanced Retinal Grading
Nagur Shareef Shaik, Teja Krishna Cherukuri, Adnan Masood +2
Diabetic retinopathy grading is inherently ordinal and long-tailed, with minority stages being scarce, heterogeneous, and clinically critical to detect accurately. Conventional met…
Dynamic Contextual Attention Network: Transforming Spatial Representations into Adaptive Insights for Endoscopic Polyp Diagnosis
Teja Krishna Cherukuri, Nagur Shareef Shaik, Sribhuvan Reddy Yellu +2
Colorectal polyps are key indicators for early detection of colorectal cancer. However, traditional endoscopic imaging often struggles with accurate polyp localization and lacks co…
GCS-M3VLT: Guided Context Self-Attention based Multi-modal Medical Vision Language Transformer for Retinal Image Captioning
Teja Krishna Cherukuri, Nagur Shareef Shaik, Jyostna Devi Bodapati +1
Retinal image analysis is crucial for diagnosing and treating eye diseases, yet generating accurate medical reports from images remains challenging due to variability in image qual…