# 3D Deep Learning on Medical Images: A Review

arXiv: https://arxiv.org/abs/2004.00218 · HTML: https://mlsanity.com/paper/2004.00218/

## Key facts

- Cited by 76 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2020.

## Built on by (most cited first, 30 of 76)

- [Domain Adaptation for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2102.09508/) (2021)
- [MAISI: Medical AI for Synthetic Imaging](https://mlsanity.com/paper/2409.11169/) (2024)
- [Deep Reinforcement Learning in Computer Vision: A Comprehensive Survey](https://mlsanity.com/paper/2108.11510/) (2021)
- [PointBA: Towards Backdoor Attacks in 3D Point Cloud](https://mlsanity.com/paper/2103.16074/) (2021)
- [Federated Learning for Multi-Center Imaging Diagnostics: A Study in Cardiovascular Disease](https://mlsanity.com/paper/2107.03901/) (2021)
- [Exploring the Devil in Graph Spectral Domain for 3D Point Cloud Attacks](https://mlsanity.com/paper/2202.07261/) (2022)
- [VoxelHop: Successive Subspace Learning for ALS Disease Classification Using Structural MRI](https://mlsanity.com/paper/2101.05131/) (2021)
- [Medical Image Segmentation with 3D Convolutional Neural Networks: A Survey](https://mlsanity.com/paper/2108.08467/) (2021)
- [Distribution-Free Federated Learning with Conformal Predictions](https://mlsanity.com/paper/2110.07661/) (2021)
- [Point Cloud Attacks in Graph Spectral Domain: When 3D Geometry Meets Graph Signal Processing](https://mlsanity.com/paper/2207.13326/) (2022)
- [Heterogeneous Graph Learning for Multi-modal Medical Data Analysis](https://mlsanity.com/paper/2211.15158/) (2022)
- [Quantum-classical convolutional neural networks in radiological image classification](https://mlsanity.com/paper/2204.12390/) (2022)
- [Med-2E3: A 2D-Enhanced 3D Medical Multimodal Large Language Model](https://mlsanity.com/paper/2411.12783/) (2024)
- [Generative Enhancement for 3D Medical Images](https://mlsanity.com/paper/2403.12852/) (2024)
- [An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works](https://mlsanity.com/paper/2103.03081/) (2021)
- [Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model](https://mlsanity.com/paper/2505.04522/) (2025)
- [Improved Brain Age Estimation with Slice-based Set Networks](https://mlsanity.com/paper/2102.04438/) (2021)
- [Applications of Generative Adversarial Networks in Neuroimaging and Clinical Neuroscience](https://mlsanity.com/paper/2206.07081/) (2022)
- [Argus: Benchmarking and Enhancing Vision-Language Models for 3D Radiology Report Generation](https://mlsanity.com/paper/2406.07146/) (2024)
- [CT-AGRG: Automated Abnormality-Guided Report Generation from 3D Chest CT Volumes](https://mlsanity.com/paper/2408.11965/) (2024)
- [Masked LoGoNet: Fast and Accurate 3D Image Analysis for Medical Domain](https://mlsanity.com/paper/2402.06190/) (2024)
- [Applications of Large Models in Medicine](https://mlsanity.com/paper/2502.17132/) (2025)
- [Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis](https://mlsanity.com/paper/2412.03961/) (2024)
- [3D Brain and Heart Volume Generative Models: A Survey](https://mlsanity.com/paper/2210.05952/) (2022)
- [Using deep learning for an automatic detection and classification of the vascular bifurcations along the Circle of Willis](https://mlsanity.com/paper/2308.15088/) (2023)
- [A Survey on Image-text Multimodal Models](https://mlsanity.com/paper/2309.15857/) (2023)
- [Provable Multi-instance Deep AUC Maximization with Stochastic Pooling](https://mlsanity.com/paper/2305.08040/) (2023)
- [nnLandmark: A Self-Configuring Method for 3D Medical Landmark Detection](https://mlsanity.com/paper/2504.06742/) (2025)
- [COVID-19 identification from volumetric chest CT scans using a progressively resized 3D-CNN incorporating segmentation, augmentation, and class-rebalancing](https://mlsanity.com/paper/2102.06169/) (2021)
- [IB-U-Nets: Improving medical image segmentation tasks with 3D Inductive Biased kernels](https://mlsanity.com/paper/2210.15949/) (2022)

## Abstract (arXiv)

The rapid advancements in machine learning, graphics processing technologies and the availability of medical imaging data have led to a rapid increase in the use of deep learning models in the medical domain. This was exacerbated by the rapid advancements in convolutional neural network (CNN) based architectures, which were adopted by the medical imaging community to assist clinicians in disease diagnosis. Since the grand success of AlexNet in 2012, CNNs have been increasingly used in medical image analysis to improve the efficiency of human clinicians. In recent years, three-dimensional (3D) CNNs have been employed for the analysis of medical images. In this paper, we trace the history of how the 3D CNN was developed from its machine learning roots, we provide a brief mathematical description of 3D CNN and provide the preprocessing steps required for medical images before feeding them to 3D CNNs. We review the significant research in the field of 3D medical imaging analysis using 3D CNNs (and its variants) in different medical areas such as classification, segmentation, detection and localization. We conclude by discussing the challenges associated with the use of 3D CNNs in the medical imaging domain (and the use of deep learning models in general) and possible future trends in the field.