Recent advances and clinical applications of deep learning in medical image analysis

Abstract

Deep learning has received extensive research interest in developing new medical image processing algorithms, and deep learning based models have been remarkably successful in a variety of medical imaging tasks to support disease detection and diagnosis. Despite the success, the further improvement of deep learning models in medical image analysis is majorly bottlenecked by the lack of large-sized and well-annotated datasets. In the past five years, many studies have focused on addressing this challenge. In this paper, we reviewed and summarized these recent studies to provide a comprehensive overview of applying deep learning methods in various medical image analysis tasks. Especially, we emphasize the latest progress and contributions of state-of-the-art unsupervised and semi-supervised deep learning in medical image analysis, which are summarized based on different application scenarios, including classification, segmentation, detection, and image registration. We also discuss the major technical challenges and suggest the possible solutions in future research efforts.

To appear in the journal Medical Image Analysis. The registration section was revised

References in corpus (36)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Conditional Generative Adversarial Nets
• Neural Architecture Search with Reinforcement Learning
• Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?
• Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations
• Semi-Supervised Learning with Deep Generative Models
• Domain-adversarial neural networks to address the appearance variability of histopathology images
• Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation
• Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders
• Med3D: Transfer Learning for 3D Medical Image Analysis
• Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model
• Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification
• UNETR: Transformers for 3D Medical Image Segmentation
• A Theoretical Analysis of Contrastive Unsupervised Representation Learning
• CenterNet: Keypoint Triplets for Object Detection
• The Importance of Skip Connections in Biomedical Image Segmentation
• TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation
• Cascaded Partial Decoder for Fast and Accurate Salient Object Detection
• You Only Learn Once: Universal Anatomical Landmark Detection
• CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation
• Accurate Pulmonary Nodule Detection in Computed Tomography Images Using Deep Convolutional Neural Networks
• Dual Path Networks
• Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction
• Semi-Supervised Deep Learning for Abnormality Classification in Retinal Images
• MedAug: Contrastive learning leveraging patient metadata improves representations for chest X-ray interpretation
• Self-supervised Pre-training with Hard Examples Improves Visual Representations
• Cross-view Relation Networks for Mammogram Mass Detection
• Self-Supervised Learning for Spinal MRIs
• Self-supervised Feature Learning for 3D Medical Images by Playing a Rubik's Cube
• Unsupervised Deformable Image Registration Using Cycle-Consistent CNN
• Revisiting Rubik's Cube: Self-supervised Learning with Volume-wise Transformation for 3D Medical Image Segmentation
• PAFNet: An Efficient Anchor-Free Object Detector Guidance
• Improving Deep Lesion Detection Using 3D Contextual and Spatial Attention
• ULDor: A Universal Lesion Detector for CT Scans with Pseudo Masks and Hard Negative Example Mining
• Lymph Node Gross Tumor Volume Detection and Segmentation via Distance-based Gating using 3D CT/PET Imaging in Radiotherapy
• Renal Cell Carcinoma Detection and Subtyping with Minimal Point-Based Annotation in Whole-Slide Images

Cited by in corpus (22)

• CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection
• Self-supervised learning methods and applications in medical imaging analysis: A survey
• A Foundation Language-Image Model of the Retina (FLAIR): Encoding Expert Knowledge in Text Supervision
• Is attention all you need in medical image analysis? A review
• Virtual histological staining of unlabeled autopsy tissue
• Can autism be diagnosed with AI?
• Deep Learning for Pancreas Segmentation: a Systematic Review
• A Deep Registration Method for Accurate Quantification of Joint Space Narrowing Progression in Rheumatoid Arthritis
• Few-shot learning for COVID-19 Chest X-Ray Classification with Imbalanced Data: An Inter vs. Intra Domain Study
• Towards Foundation Models and Few-Shot Parameter-Efficient Fine-Tuning for Volumetric Organ Segmentation
• Gravity Network for end-to-end small lesion detection
• Are Vision xLSTM Embedded UNet More Reliable in Medical 3D Image Segmentation?
• Robust and Explainable Framework to Address Data Scarcity in Diagnostic Imaging
• Unified Multi-modal Diagnostic Framework with Reconstruction Pre-training and Heterogeneity-combat Tuning
• MedVKAN: Efficient Feature Extraction with Mamba and KAN for Medical Image Segmentation
• Towards the use of multiple ROIs for radiomics-based survival modelling: finding a strategy of aggregating lesions
• Self-Supervised Representation Learning for Nerve Fiber Distribution Patterns in 3D-PLI
• Adaptive Input-image Normalization for Solving the Mode Collapse Problem in GAN-based X-ray Images
• VIS-MAE: An Efficient Self-supervised Learning Approach on Medical Image Segmentation and Classification
• Label-free evaluation of lung and heart transplant biopsies using tissue autofluorescence-based virtual staining
• AGNES: Abstraction-guided Framework for Deep Neural Networks Security
• Trustworthy Data-driven Chronological Age Estimation from Panoramic Dental Images