161 citations · 173 across the 12 of their papers we have counts for
11 papers · 1 filter
CA: Coupling Spatial Evidence with Clinical Priors via Co-occurrence Aware Class Attention for Multi-Label Chest X-Ray Classification
Akash Gogineni, Nagur Shareef Shaik, Aasrith Mandava +2
Thoracic pathologies rarely occur in isolation, yet standard multi-label classifiers rely on shared global descriptors, discarding \emph{where} findings lie and \emph{how} they co-…
Disentangling Co-Occurring Retinal Pathologies with Saliency-Guided Sparse Expert Routing
Nagur Shareef Shaik, Jeongwoo Park, Yeong-Jin Kim +3
Retinal fundus images frequently exhibit multiple co-occurring pathologies, yet standard deep learning classifiers apply static, identical computation to every image regardless of…
Region-Affinity Attention for Whole-Slide Breast Cancer Classification in Deep Ultraviolet Imaging
Nagur Shareef Shaik, Teja Krishna Cherukuri, Dong Hye Ye
Breast cancer diagnosis demands rapid and precise tools, yet traditional histopathological methods often fall short in intra-operative settings. Deep Ultraviolet (DUV) fluorescence…
DREAM: Dynamic Retinal Enhancement with Adaptive Multi-modal Fusion for Expert Precision Medical Report Generation
Nagur Shareef Shaik, Teja Krishna Cherukuri, Dong Hye Ye
Automating medical reports for retinal images requires a sophisticated blend of visual pattern recognition and deep clinical knowledge. Current Large Vision-Language Models (LVLMs)…
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…
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…
Multi-modal Imaging Genomics Transformer: Attentive Integration of Imaging with Genomic Biomarkers for Schizophrenia Classification
Nagur Shareef Shaik, Teja Krishna Cherukuri, Vince D. Calhoun +1
Schizophrenia (SZ) is a severe brain disorder marked by diverse cognitive impairments, abnormalities in brain structure, function, and genetic factors. Its complex symptoms and ove…
M3T: Multi-Modal Medical Transformer to bridge Clinical Context with Visual Insights for Retinal Image Medical Description Generation
Nagur Shareef Shaik, Teja Krishna Cherukuri, Dong Hye Ye
Automated retinal image medical description generation is crucial for streamlining medical diagnosis and treatment planning. Existing challenges include the reliance on learned ret…
Guided Context Gating: Learning to leverage salient lesions in retinal fundus images
Teja Krishna Cherukuri, Nagur Shareef Shaik, Dong Hye Ye
Effectively representing medical images, especially retinal images, presents a considerable challenge due to variations in appearance, size, and contextual information of pathologi…
Spatial Sequence Attention Network for Schizophrenia Classification from Structural Brain MR Images
Nagur Shareef Shaik, Teja Krishna Cherukuri, Vince Calhoun +1
Schizophrenia is a debilitating, chronic mental disorder that significantly impacts an individual's cognitive abilities, behavior, and social interactions. It is characterized by s…