13 papers
FedDP-PALD: A Privacy-Preserving Federated Latent Diffusion Framework with Prototype Aggregation for Medical Data Synthesis
Md. Sajeebul Islam Sk., Khan Enaet Hossain, Md. Mehedi Hasan Shawon
Medical images and physiological signals provide valuable information for accurate diagnosis. Developing diagnostic models often requires patient data from multiple institutions, a…
FunnelNet: An End-to-End Deep Learning Framework to Monitor Digital Heart Murmur in Real-Time
Md Jobayer, Md. Mehedi Hasan Shawon, Md Zakir Hossain +4
Heart murmurs are abnormal sounds caused by turbulent blood flow in the heart. Several diagnostic methods are available to detect heart murmurs and their severity, including cardia…
Attentive Dilated Convolution for Automatic Sleep Staging using Force-directed Layout
Md Jobayer, Md Mehedi Hasan Shawon, Tasfin Mahmud +2
Sleep stages play an important role in identifying sleep patterns and diagnosing sleep disorders. In this study, we present an automated sleep stage classifier called the Attentive…
An Explainable Vision-Language Model Framework with Adaptive PID-Tversky Loss for Lumbar Spinal Stenosis Diagnosis
Md. Sajeebul Islam Sk., Md. Mehedi Hasan Shawon, Md. Golam Rabiul Alam
Lumbar Spinal Stenosis (LSS) diagnosis remains a critical clinical challenge, with diagnosis heavily dependent on labor-intensive manual interpretation of multi-view Magnetic Reson…
Less Is More? Selective Visual Attention to High-Importance Regions for Multimodal Radiology Summarization
Mst. Fahmida Sultana Naznin, Adnan Ibney Faruq, Mushfiqur Rahman +3
Automated radiology report summarization aims to distill verbose findings into concise clinical impressions, but existing multimodal models often struggle with visual noise and fai…
Interpretable Heart Disease Prediction via a Weighted Ensemble Model: A Large-Scale Study with SHAP and Surrogate Decision Trees
Md Abrar Hasnat, Md Jobayer, Md. Mehedi Hasan Shawon +1
Cardiovascular disease (CVD) remains a critical global health concern, demanding reliable and interpretable predictive models for early risk assessment. This study presents a large…