# Going Deep in Medical Image Analysis: Concepts, Methods, Challenges and Future Directions

arXiv: https://arxiv.org/abs/1902.05655 · HTML: https://mlsanity.com/paper/1902.05655/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Feb 2019.

## Built on by (most cited first, 24 of 24)

- [Implicit Neural Representation in Medical Imaging: A Comparative Survey](https://mlsanity.com/paper/2307.16142/) (2023)
- [Self-supervised learning methods and applications in medical imaging analysis: A survey](https://mlsanity.com/paper/2109.08685/) (2021)
- [Towards a Guideline for Evaluation Metrics in Medical Image Segmentation](https://mlsanity.com/paper/2202.05273/) (2022)
- [Kvasir-VQA: A Text-Image Pair GI Tract Dataset](https://mlsanity.com/paper/2409.01437/) (2024)
- [Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models](https://mlsanity.com/paper/2103.00429/) (2021)
- [A Survey of Neural Trojan Attacks and Defenses in Deep Learning](https://mlsanity.com/paper/2202.07183/) (2022)
- [Medical Slice Transformer: Improved Diagnosis and Explainability on 3D Medical Images with DINOv2](https://mlsanity.com/paper/2411.15802/) (2024)
- [Local Concept Embeddings for Analysis of Concept Distributions in Vision DNN Feature Spaces](https://mlsanity.com/paper/2311.14435/) (2023)
- [Prompt to Polyp: Medical Text-Conditioned Image Synthesis with Diffusion Models](https://mlsanity.com/paper/2505.05573/) (2025)
- [Deep Learning for Reaction-Diffusion Glioma Growth Modelling: Towards a Fully Personalised Model?](https://mlsanity.com/paper/2111.13404/) (2021)
- [Classification of White Blood Cells Using Machine and Deep Learning Models: A Systematic Review](https://mlsanity.com/paper/2308.06296/) (2023)
- [Edge-competing Pathological Liver Vessel Segmentation with Limited Labels](https://mlsanity.com/paper/2108.00384/) (2021)
- [CLIN-LLM: A Safety-Constrained Hybrid Framework for Clinical Diagnosis and Treatment Generation](https://mlsanity.com/paper/2510.22609/) (2025)
- [Deep Integrated Pipeline of Segmentation Guided Classification of Breast Cancer from Ultrasound Images](https://mlsanity.com/paper/2110.14013/) (2021)
- [MISm: A Medical Image Segmentation Metric for Evaluation of weak labeled Data](https://mlsanity.com/paper/2210.13642/) (2022)
- [Privacy-preserving datasets by capturing feature distributions with Conditional VAEs](https://mlsanity.com/paper/2408.00639/) (2024)
- [Resetting the baseline: CT-based COVID-19 diagnosis with Deep Transfer Learning is not as accurate as widely thought](https://mlsanity.com/paper/2108.05649/) (2021)
- [Disentangling Hippocampal Shape Variations: A Study of Neurological Disorders Using Mesh Variational Autoencoder with Contrastive Learning](https://mlsanity.com/paper/2404.00785/) (2024)
- [Requirement analysis for an artificial intelligence model for the diagnosis of the COVID-19 from chest X-ray data](https://mlsanity.com/paper/2110.12464/) (2021)
- [Pixel-global Self-supervised Learning with Uncertainty-aware Context Stabilizer](https://mlsanity.com/paper/2210.00646/) (2022)
- [Pre-text Representation Transfer for Deep Learning with Limited Imbalanced Data : Application to CT-based COVID-19 Detection](https://mlsanity.com/paper/2301.08888/) (2023)
- [Shape-Margin Knowledge Augmented Network for Thyroid Nodule Segmentation and Diagnosis](https://mlsanity.com/paper/2308.15386/) (2023)
- [When do Convolutional Neural Networks Stop Learning?](https://mlsanity.com/paper/2403.02473/) (2024)
- [Enhancing Deep Learning Model Explainability in Brain Tumor Datasets using Post-Heuristic Approaches](https://mlsanity.com/paper/2404.19568/) (2024)

## Abstract (arXiv)

Medical Image Analysis is currently experiencing a paradigm shift due to Deep Learning. This technology has recently attracted so much interest of the Medical Imaging community that it led to a specialized conference in `Medical Imaging with Deep Learning' in the year 2018. This article surveys the recent developments in this direction, and provides a critical review of the related major aspects. We organize the reviewed literature according to the underlying Pattern Recognition tasks, and further sub-categorize it following a taxonomy based on human anatomy. This article does not assume prior knowledge of Deep Learning and makes a significant contribution in explaining the core Deep Learning concepts to the non-experts in the Medical community. Unique to this study is the Computer Vision/Machine Learning perspective taken on the advances of Deep Learning in Medical Imaging. This enables us to single out `lack of appropriately annotated large-scale datasets' as the core challenge (among other challenges) in this research direction. We draw on the insights from the sister research fields of Computer Vision, Pattern Recognition and Machine Learning etc.; where the techniques of dealing with such challenges have already matured, to provide promising directions for the Medical Imaging community to fully harness Deep Learning in the future.