Precise localization of pathologies in endoscopy is a critical task in medical imaging, with deep learning (DL) frameworks now serving as the primary methodology for automated lesion detection. Today, generic object detection methods, such as YOLO, provide state-of-the-art performance in this context. However, their robustness often decreases under real-world clinical conditions due to variations across endoscopic devices, intestinal content, dataset bias, and the presence of small or low-contrast lesions. Motivated by these challenges, we propose a novel polyp detection framework based on a Attention Residual U-Net offering enhanced polyp localization. Instead of directly regressing bounding boxes from global feature representations, the proposed method learns lesion-focused spatial representations and converts them into bounding-box predictions during inference. Unlike conventional encoder-decoder localization frameworks that directly propagate all encoder activations into the decoding pathway, the proposed method selectively suppresses irrelevant background responses before feature reconstruction. This encourages the network to focus on lesion-relevant spatial evidence under heterogeneous imaging conditions. To achieve this, the framework integrates residual feature propagation, attention-guided encoder filtering, and multi-scale contextual learning. The proposed framework was evaluated on multiple public endoscopic datasets acquired using different devices. The comparative experimental results validate its effectiveness over the relevant state-of-the-art methods, with an improvement up to 22.0% in terms of the area under the receiving operating characteristic (AUC).
Synthetic medical tabular data generation has become essential for developing and validating computer-based medical systems (CBMSs) when real clinical data is restricted due to privacy, ethical, or data availability limitations. Existing probabilistic and deep generative models often lack interpretability and fail to preserve clinically meaningful dependencies, limiting their suitability for safety-critical applications. This paper proposes a novel application of Fuzzy Cognitive Maps (FCMs) in a framework for synthetic medical tabular data generation with explicit causality and privacy preservation. Clinical features are described using linguistically interpretable fuzzy sets, and inter-feature dependencies are encoded as FCM edge weights computed from fuzzy set intersections. Synthetic patient records are generated by propagating randomly initialized linguistic activation vectors through the FCM until convergence, followed by defuzzification to produce clinically coherent numerical values. The approach natively handles mixed data types, and domain constraints common in health records. Experimental evaluation on UCI medical benchmark datasets demonstrates competitive performance under a Train-on-Synthetic-Test-on-Real (TSTR) protocol. The proposed method achieves accuracy of up to 0.81 and AUROC of up to 0.90 on the Heart Disease dataset, matching or exceeding TVAE and Gaussian Copula baselines while running exclusively on CPU. Fidelity metrics including KS Complement (up to 0.91) and Correlation Similarity (up to 0.95) confirm strong statistical coherence, and DCR Baseline Protection scores consistently exceed those of TVAE, confirming adequate privacy guarantees. These results demonstrate that causally grounded, interpretable fuzzy modeling offers a computationally efficient and transparent alternative to deep generative models for trustworthy synthetic data generation in CBMSs.