Volumetric medical imaging has redefined modern healthcare, enabling precise diagnosis, prognosis, and treatment planning. During the past decade, the field has undergone a paradigm shift from classical deep learning architectures to multimodal, agent-driven AI systems capable of uncovering rich volumetric biomarkers and utilizing heterogeneous data for predictive and generative modeling. Existing surveys are fragmented, focusing on specific models or tasks instead of offering a unified view of volumetric learning evolution. This study traces the evolution from classical models (Convolutional Neural Networks, Recurrent Neural Networks, and transformers) to generative approaches (Variational Autoencoders, Generative Adversarial Networks, and diffusion models) and finally to foundation models and AI-agents that enable advanced reasoning and adaptive clinical workflows. As the reported performance varies substantially across datasets, imaging modalities, and evaluation protocols, this review emphasizes methodological evolution, representative innovations, and practical implications rather than direct numerical ranking of competing architectures. For each paradigm, we critically assess methodological innovations, strengths, limitations, and comparative performance in segmentation, classification, detection, reconstruction, and report generation. Beyond synthesizing progress, we identify persistent challenges, including data scarcity, generalization between institutions, and clinical trustworthiness, and outline emerging frontiers in multimodal fusion, explainable AI, and human–AI collaboration. This review provides a unified framework for understanding the evolution of volumetric medical imaging and offers actionable insights for researchers, clinicians, and industry practitioners, contributing to the development of reliable, interpretable, and clinically deployable next-generation medical AI systems. To support further research, we provide a GitHub repository that includes popular 3D medical imaging datasets with recent 3D models in our shared GitHub repository (https://github.com/Owais-CodeHub/3D-Medical-Imaging-Review).
Muhammad Owais, Muhammad Zubair, Daniya Najiha Abdul Kareem et al.· Archives of Computational Me...· 8 citations· ⚡1
Accurate classification of the seabed defects is crucial in safeguarding the safety, reliability, and maintenance of underwater infrastructure, including pipelines, cables, and offshore platforms. Classification of underwater images remains a challenging task due to issues such as low visibility, scattering, color distortion, and multi-scale variations in underwater environments. To address these challenges, this study proposes IEResViT, a novel fusion-based hybrid architecture that integrates a Depthwise Inception Convolutional Neural Network (CNN) with a modified Vision Transformer (ViT) framework. The proposed model replaces the traditional multilayer perceptron (MLP) block in the transformer encoder with a ResNet-based residual connection (ResMLP) to enhance hierarchical feature extraction and reduce computational complexity. The architecture processes images through parallel CNN and transformer branches to capture both local and global feature representations, followed by feature fusion and classification. The proposed model was evaluated on the Aquatic Defect dataset, AQUA20 dataset and Marine_PULSE dataset, achieving high classification performance with an accuracy of 98.88% 98.65% and 95.65% respectively, outperforming several state-of-the-art deep learning models while using fewer parameters and reduced computational cost. The results demonstrate that the IEResViT model provides an efficient and lightweight solution for reliable underwater defect image classification.
Underwater infrastructure, such as pipelines, communication cables, offshore wind farms, and marine habitats, is a crucial interface on the seabed. Natural processes and human activities may cause seabed defects, e.g., cracks and erosion scars, or sediment liquefaction areas or uncovered buried utilities, over time. Early and precise diagnosis of these defects is critical in preventive maintenance, environmental safety, and navigation. Traditional seabed relies on manual interpretation, and its time consuming and labour-intensive. Recent advancements in machine learning help in automated detection. The graphical abstract presents a deep learning model to classify the aquatic defect images. This article presents the IEResViT model, which uses two parallel branches: a depthwise inception CNN branch that extracts local spatial features, and a ResMLP ViT branch that captures global dependencies through multi-head self-attention mechanisms. The extracted local and global features are then fused together, enabling enhanced representation of complex underwater defect image characteristics. The fused features are subsequently passed to fully connected dense layers, followed by a softmax classifier to predict the defect category. The Aquatic defect image dataset, AQUA20 dataset, and Marine_PULSE dataset are used to evaluate the performance of the model, and achieved an accuracy rate of 98.88%, 98.65%, and 95.65% respectively.
Sk Mahmudul Hassan, Kumar Sekhar Roy, Mehbub Alam et al.· Earth Systems and Environmen...· 0 citations
