Bumshik Lee

Afnan Ghafoor, Seong-Yong Moon, Bumshik Lee

This paper proposed a cutting-edge multiclass teeth segmentation architecture that integrates an M-Net-like structure with Swin Transformers and a novel component named Teeth Attention Block (TAB). Existing teeth image segmentation methods have issues with less accurate and unreliable segmentation outcomes due to the complex and varying morphology of teeth, although teeth segmentation in dental panoramic images is essential for dental disease diagnosis. We propose a novel teeth segmentation model incorporating an M-Net-like structure with Swin Transformers and TAB. The proposed TAB utilizes a unique attention mechanism that focuses specifically on the complex structures of teeth. The attention mechanism in TAB precisely highlights key elements of teeth features in panoramic images, resulting in more accurate segmentation outcomes. The proposed architecture effectively captures local and global contextual information, accurately defining each tooth and its surrounding structures. Furthermore, we employ a multiscale supervision strategy, which leverages the left and right legs of the U-Net structure, boosting the performance of the segmentation with enhanced feature representation. The squared Dice loss is utilized to tackle the class imbalance issue, ensuring accurate segmentation across all classes. The proposed method was validated on a panoramic teeth X-ray dataset, which was taken in a real-world dental diagnosis. The experimental results demonstrate the efficacy of our proposed architecture for tooth segmentation on multiple benchmark dental image datasets, outperforming existing state-of-the-art methods in objective metrics and visual examinations. This study has the potential to significantly enhance dental image analysis and contribute to advances in dental applications.

Abdul Rahman, Bumshik Lee

Osteoporosis silently erodes skeletal integrity worldwide; however, early detection through imaging can prevent most fragility fractures. Artificial intelligence (AI) methods now mine routine Dual-energy X-ray Absorptiometry (DXA), X-ray, Computed Tomography (CT), and Magnetic Resonance Imaging (MRI) scans for subtle, clinically actionable markers, but the literature is fragmented. This survey unifies the field through a tri-axial framework that couples imaging modalities with clinical tasks and AI methodologies (classical machine learning, convolutional neural networks (CNNs), transformers, self-supervised learning, and explainable AI). Following a concise clinical and technical primer, we detail our Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-guided search strategy, introduce the taxonomy via a roadmap figure, and synthesize cross-study insights on data scarcity, external validation, and interpretability. By identifying emerging trends, open challenges, and actionable research directions, this review provides AI scientists, medical imaging researchers, and musculoskeletal clinicians with a clear compass to accelerate rigorous, patient-centered innovation in osteoporosis care. The project page of this survey can also be found on Github.

Aradhana Mishra, Bumshik Lee

Diffusion-model-based image super-resolution techniques often face a trade-off between realistic image generation and computational efficiency. This issue is exacerbated when inference times by decreasing sampling steps, resulting in less realistic and hazy images. To overcome this challenge, we introduce a novel diffusion model named PixelBoost that underscores the significance of embracing the stochastic nature of Brownian motion in advancing image super-resolution, resulting in a high degree of realism, particularly focusing on texture and edge definitions. By integrating controlled stochasticity into the training regimen, our proposed model avoids convergence to local optima, effectively capturing and reproducing the inherent uncertainty of image textures and patterns. Our proposed model demonstrates superior objective results in terms of learned perceptual image patch similarity (LPIPS), lightness order error (LOE), peak signal-to-noise ratio(PSNR), structural similarity index measure (SSIM), as well as visual quality. To determine the edge enhancement, we evaluated the gradient magnitude and pixel value, and our proposed model exhibited a better edge reconstruction capability. Additionally, our model demonstrates adaptive learning capabilities by effectively adjusting to Brownian noise patterns and introduces a sigmoidal noise sequencing method that simplifies training, resulting in faster inference speeds.