6 papers
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-…
QuantiPhy: A Quantitative Benchmark Evaluating Physical Reasoning Abilities of Vision-Language Models
Li Puyin, Tiange Xiang, Ella Mao +5
Understanding the physical world is essential for generalist AI agents. However, it remains unclear whether state-of-the-art vision perception models (e.g., large VLMs) can reason…
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…
SOE: SO(3)-Equivariant 3D MRI Encoding
Shizhe He, Magdalini Paschali, Jiahong Ouyang +3
Representation learning has become increasingly important, especially as powerful models have shifted towards learning latent representations before fine-tuning for downstream task…
Enforcing Conditional Independence for Fair Representation Learning and Causal Image Generation
Jensen Hwa, Qingyu Zhao, Aditya Lahiri +3
Conditional independence (CI) constraints are critical for defining and evaluating fairness in machine learning, as well as for learning unconfounded or causal representations. Tra…