Breast cancer remains a leading cause of morbidity and mortality worldwide. Histopathology, particularly the analysis of nuclear morphology in tissue samples, is critical for diagnosing and understanding the progression of breast cancer. Accurate nuclei segmentation plays a pivotal role in enabling detailed assessment of nuclear size, shape, and distribution patterns, which are essential for clinical diagnosis. However, traditional single-scale segmentation methods often fail to achieve this accuracy due to their inability to preserve fine details, capture broader contextual information, distinguish overlapping nuclei, and handle the inherent variability in nuclear morphology across different cell types. To address these challenges, we propose a multi-stream encoder-decoder architecture named M3-Net++ (Multi-Scale, Multi-Level, Multi-Stream Network), a novel deep learning model tailored for nuclei segmentation in histopathology images. M3-Net++ integrates both global and local tissue features for improved segmentation accuracy. The inclusion of Feature Refinement and Redundancy Elimination (FRRE) module further emphasizes critical features, while skip connections preserve high-resolution spatial information. To overcome challenges such as overlapping nuclei and class imbalance, we introduce a Hybrid Segmentation Loss (HSL) function. M3-Net++ achieves state-of-the-art performance on three benchmark datasets-ER-IHC, MoNuSAC, and CoNSeP-achieving Dice scores of 0.875, 0.871, and 0.872, and Panoptic Quality (PQ) scores of 0.730, 0.700, and 0.588, respectively, outperforming models such as HoVer-Net and SMILE. Despite these improvements, M3-Net++ remains computationally efficient, requiring only 46.18M parameters, 7.2 GB of peak GPU memory, and 0.119 s of inference time per 256×256 patch. These results highlight the robustness, adaptability, and clinical potential of M3-Net++ for breast cancer histopathology image analysis.

Background: Accurate differentiation of kidney diseases such as cysts, tumors, stones, and normal tissue from computed tomography (CT) images remains challenging due to overlapping visual characteristics and variability in data distributions. While deep learning approaches have shown promising results, many existing studies rely on image-level data splitting and focus primarily on accuracy, which may lead to overly optimistic performance and limited clinical reliability. Methods: This study proposes KDH-Net (Kidney Disease Hybrid Network), a hybrid deep learning framework for multiclass kidney disease characterization that integrates EfficientNetB0, ResNet50, and MobileNetV2 through feature-level fusion. A two-stage training strategy is adopted to enhance optimization stability. To ensure realistic performance assessment, experiments on the primary dataset are conducted under a patient-level evaluation protocol, eliminating potential data leakage. The framework further incorporates calibration analysis, statistical validation, and explainable artificial intelligence to evaluate prediction reliability and interpretability. Results: On the patient-level dataset, KDH-Net achieves an overall accuracy of 0.93 with a macro-average F1-score of 0.91, demonstrating balanced performance across all classes. Confidence analysis indicates meaningful alignment between prediction confidence and correctness, while Grad-CAM visualizations highlight anatomically relevant regions associated with each class. Conclusions: The results demonstrate that KDH-Net provides a stable, reliable, and interpretable framework for kidney CT characterization. The proposed system is designed to support clinical decision-making by offering trustworthy predictions under realistic evaluation conditions, rather than replacing clinical expertise.

Background: Nasopharyngeal carcinoma (NPC) presents significant diagnostic and therapeutic challenges, often due to late-stage detection and its complex anatomical location. The increasing integration of artificial intelligence (AI) into oncology offers potential opportunities to enhance the precision of NPC management. This systematic review aims to synthesise the current evidence of AI applications in NPC diagnosis, prognostication, and treatment planning. Methods: A systematic literature search was conducted following PRISMA guidelines across multiple databases (PubMed, Scopus, Embase, Google Scholar, IEEE Xplore) for studies published up to June 2025. From an initial pool of 2549 articles, 55 studies meeting the inclusion criteria were selected for qualitative analysis. The review focuses on AI models applied to key diagnostic modalities: computed tomography (CT), magnetic resonance imaging (MRI), and histopathological whole-slide images (WSI). Results: AI, particularly deep learning (DL), shows promising performance in automating critical tasks across all modalities. For CT and MRI, models have been reported to achieve accurate tumor and organ-at-risk segmentation, potentially supporting radiotherapy planning, and show strong performance in predicting survival outcomes and treatment toxicity. In digital pathology, AI enables automated diagnosis and facilitates the extraction of prognostic “pathomic” features from WSIs, with some studies suggesting performance comparable to or exceeding traditional radiomics. The most significant advances are seen in multimodal AI systems that integrate radiological, pathological, and clinical data, which, in some studies, show modest improvements in prognostic performance compared to single-modality approaches. However, these findings are preliminary, as none of the reviewed multimodal models underwent rigorous external validation in large, multi-center cohorts. Reported performance varies considerably across studies, and claims of superiority should be interpreted with caution.