Deep-learning models can achieve strong chest X-ray (CXR) classification performance without establishing whether their predictions predominantly rely on pulmonary image content. This study evaluates pulmonary attribution containment as an anatomy-related reliability property distinct from diagnostic performance. We pr...
A. al Mamun, Md. Nasif Khansur, Md. Ashraful Hossen Akash et al.· 0 citations
The advanced retinal disease diagnosing imaging modality, optical coherence tomography (OCT), encounters a lack of automation because of the high expenses for annotations performed by specialists. The use of SSL solves the problem of insufficient annotations using unlabeled B-scans; however, most of the current techniq...
Md. Ashraful Hossen Akash, Shyla Afroge, A. al Mamun et al.· 0 citations
Findings support the feasibility of bounded, database-grounded, and traceable agent orchestration for non-diagnostic skincare assistance, but do not establish clinical readiness, external generalization, formal privacy guarantees, or universal safety.
Muhammad Muhtasim Shahriar, Abdulla Sayem, Tze Hui Liew et al.· 0 citations
This study establishes the Diabetic Retinopathy Latent Diffusion Synthesizer (DR-LDS) as a highly resource-efficient solution to the medical data bottleneck by leveraging a fine-tuned Variational Autoencoder for domain-adapted latent space compression, alongside an optimized U-Net.
Touhid Alam, Tze Hui Liew, M. Morol et al.· Discover Artificial Intellig...· 0 citations
Recent advances in large language models (LLMs) and vision transformers have enabled multimodal systems that integrate clinical text with medical imaging for diagnostic decision-making. While these systems show promising results on benchmark datasets in well-resourced research settings, their applicability in low-resou...
Kahakashan Ashraf, Md.Hamid Hosen, N. Farah et al.· Frontiers in Digital Health· 0 citations
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