[Medical ML] Vision Transformers in Radiology: From Image Patches to Clinical Decisions

# Vision Transformers in Radiology: From Image Patches to Clinical Decisions

**Author:** Oleh Ivchenko
**Published:** February 8, 2026
**Series:** ML for Medical Diagnosis Research
**Article:** 14 of 35

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## Executive Summary

Vision Transformers (ViTs) have emerged as a transformative architecture in medical imaging, challenging the decade-long dominance of Convolutional Neural Networks (CNNs). Unlike CNNs that build understanding through hierarchical local feature extraction, ViTs treat images as sequences of patches and leverage self-attention mechanisms to capture global context from the first layer. This comprehensive analysis examines the current state of ViTs in radiology, their clinical performance, and their positioning for Ukrainian healthcare integration.

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## Understanding Vision Transformers

### The Paradigm Shift from CNNs

Convolutional Neural Networks have dominated medical image analysis since 2012, leveraging local receptive fields and hierarchical feature extraction. Vision Transformers, introduced by Dosovitskiy et al. in 2020, fundamentally reimagine this approach by treating images as sequences of patches—similar to how language models process word tokens.

“`mermaid
graph LR
A[Input Image] –> B[Conv Layer 1
B –> C[Conv Layer 2
C –> D[Conv Layer 3+
D –> E[Classification]
F[Input Image] –> G[Split into
G –> H[Linear Embedding
“`

### How Vision Transformers Process Medical Images

The ViT architecture processes radiological images through several key steps:

1. **Patch Division**: An input image (e.g., 224×224 pixels) is divided into fixed-size patches (typically 16×16), resulting in 196 patches
2. **Linear Embedding**: Each patch is flattened and projected to a D-dimensional embedding space
3. **Position Encoding**: Learnable positional embeddings are added to retain spatial information
4. **Self-Attention**: Multi-head self-attention allows every patch to attend to every other patch
5. **Classification**: A special [CLS] token aggregates information for final prediction

“`mermaid
graph TD
A[Medical Image
224x224x3] –> B[Patch Extraction
B –> C[Patch Embedding
C –> D[Add Position
D –> E[Prepend CLS
E –> F[Multi-Head
“`

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## Clinical Performance Comparison

### Systematic Review Findings (2024-2025)

A comprehensive systematic review published in the *Journal of Medical Systems* (September 2024) analyzed 36 studies comparing ViTs and CNNs across multiple medical imaging modalities. The findings reveal nuanced performance patterns:

### Key Observations

**Where ViTs Excel:**
– Complex spatial relationships (brain MRI, tumor boundaries)
– Limited dataset scenarios (paradoxically, with proper pretraining)
– Global context tasks (lung disease classification across entire chest)
– Long-range dependency detection

**Where CNNs Still Lead:**
– Large, well-annotated datasets (chest X-rays)
– Edge detection and local feature tasks
– Real-time inference requirements
– Resource-constrained deployments

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## Advanced ViT Architectures for Radiology

### Evolution of Medical Vision Transformers

“`mermaid
timeline
title Evolution of Vision Transformers in Medical Imaging

2020 : ViT Original
: “Patches as Tokens”
: Requires huge datasets

2021 : DeiT (Distillation)
: Better small-data performance
: Knowledge transfer from CNNs

2021 : Swin Transformer
: Shifted windows
: Linear complexity O(N)

2022 : DINO (Self-Supervised)
: No labels needed
: Attention = Segmentation

2023 : MedViT
: Generalized medical imaging
: Robust to distribution shifts

2025 : MedViT V2 + KAN
: KAN-integrated architecture
: 6.1% improvement over Swin
“`

### Swin Transformer: The Efficiency Champion

The Swin Transformer addresses ViT’s quadratic complexity through hierarchical shifted windows:

### MedViT V2: State-of-the-Art (2025)

The Medical Vision Transformer V2, incorporating Kolmogorov-Arnold Network (KAN) layers, represents the current pinnacle:

– **6.1% higher accuracy** than Swin-Base on medical benchmarks
– **Dilated Neighborhood Attention (DiNA)** for expanded receptive fields
– **Lowest FLOPs** among comparable models
– **Feature collapse resistance** when scaling up

—

## Self-Supervised Learning: The Data Bottleneck Solution

### DINO and MAE for Medical Imaging

🧠 Self-Supervised Learning Revolution Traditional ViTs require millions of labeled images. Self-supervised methods like DINO (Self-Distillation with No Labels) and MAE (Masked Autoencoders) enable training on vast unlabeled medical image corpora, then fine-tuning with minimal supervision. Vision transformers in radiology for clinical decisions Key Insight: DINO attention maps naturally segment objects—the model learns to “look at” tumors and lesions without being told where they are.

“`mermaid
graph LR
A[Unlabeled
Millions] –> B[DINO/MAE
B –> C[Pretrained
C –> D[Small Labeled
D –> E[Fine-tuned
E –> F[Clinical
“`

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## Explainability: The Clinical Trust Factor

### Attention Maps vs Grad-CAM

One of ViTs’ key advantages in clinical adoption is inherent explainability through attention mechanisms:

### Clinical Validation Study (October 2025)

A recent study evaluating ViT explainability with radiologists found:

– **ViT attention maps** correlate better with expert annotations for tumor localization
– **DINO pretraining** produces the most clinically meaningful attention patterns
– Swin Transformer provides efficient attention visualization with linear complexity
– **Gradient Attention Rollout** emerged as the most reliable visualization technique

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## Hybrid Architectures: The Practical Middle Ground

### Combining CNN and ViT Strengths

A 2024 systematic review of 34 hybrid architectures (PRISMA guidelines) identified optimal combinations:

“`mermaid
graph TD
A[Medical Image Input] –> B[CNN Stem
B –> C[Transformer Encoder
C –> D[Task-Specific Head]
E[CNN Benefits:
F[ViT Benefits:
E –> B
“`

### Leading Hybrid Models for Radiology

| Model | Architecture | Key Innovation | Medical Performance |
|——-|————-|—————-|———————|
| **ConvNeXt** | Modernized CNN with ViT training | Depth-wise convolution, ViT training tricks | Competitive with pure ViTs |
| **CoAtNet** | CNN stem + Transformer | Efficient attention integration | State-of-the-art on multiple tasks |
| **MaxViT** | Multi-axis attention | Block + Grid attention | Excellent for 3D medical images |
| **TransUNet** | U-Net with Transformer | Encoder-decoder with attention | Leading segmentation model |

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## Ukrainian Implementation Considerations

### Infrastructure Requirements

### Language Localization for Ukrainian

ViT-based systems with multimodal capabilities (like CLIP variants) can be fine-tuned for Ukrainian-language report generation, combining visual analysis with localized clinical terminology.

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## Recommendations for Clinical Integration

### Decision Framework for Architecture Selection

“`mermaid
graph TD
A[New Radiology AI Project] –> B{Dataset Size?}
B –>||Yes| D[Fine-tune DeiT/MedViT
C –>|No| E[Use CNN with
B –>|1,000 – 10,000| F{Task Type?}
F –>|Classification| G[Swin Transformer
“`

### Summary: When to Use ViTs in Radiology

| ✅ **Use Vision Transformers When** | ❌ **Prefer CNNs When** |
|————————————-|————————-|
| Complex spatial relationships matter (brain MRI, tumor boundaries) | Real-time inference is critical |
| Self-supervised pretraining is possible | Dataset is very small (<500 images) without pretrained options |
| Global context affects diagnosis | Edge deployment with limited compute |
| Attention-based explainability is valued | Local features dominate (chest X-ray) |
| Multi-modal integration is planned | Budget for compute is severely limited |

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## Conclusion

Vision Transformers represent a genuine paradigm shift in radiology AI, not merely incremental improvement. While CNNs remain dominant in FDA/CE-cleared devices today, the trajectory is clear: ViTs and hybrid architectures are achieving state-of-the-art results on increasingly complex medical imaging tasks.

For Ukrainian healthcare integration through ScanLab:

1. **Short-term**: Deploy proven CNN models (EfficientNet, ResNet) for stable, well-validated tasks
2. **Medium-term**: Adopt hybrid architectures for complex cases requiring global context
3. **Long-term**: Build institutional capability for ViT fine-tuning with Ukrainian medical data

The key insight from 2024-2025 research is that **architecture selection is task-specific**—there is no universal winner. Brain MRI analysis benefits enormously from ViT attention mechanisms (+31% over CNNs), while chest X-ray classification sees equivalent performance from both paradigms.

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## References

1. Dosovitskiy, A., et al. (2020). "An Image is Worth 16×16 Words: Transformers for Image Recognition at Scale." *arXiv:2010.11929*
2. Takahashi, S., & Sakaguchi, Y. (2024). "Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review." *Journal of Medical Systems*
3. Kawadkar, K. (2025). "Comparative Analysis of Vision Transformers and Convolutional Neural Networks for Medical Image Classification." *arXiv:2507.21156*
4. Medical Vision Transformer V2 Team (2025). "MedViT V2: Medical Image Classification with KAN-Integrated Transformers." *arXiv:2502.13693*
5. PMC Review (2025). "Vision Transformers in Medical Imaging: A Comprehensive Review." *PMC12701147*
6. Liu, Z., et al. (2021). "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows." *ICCV 2021*
7. Touvron, H., et al. (2021). "Training data-efficient image transformers & distillation through attention." *ICML 2021*
8. Caron, M., et al. (2021). "Emerging Properties in Self-Supervised Vision Transformers." *ICCV 2021* (DINO)

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*This article is part of a comprehensive research series on ML for medical diagnosis, focusing on implementation frameworks for Ukrainian healthcare. Next article: Hybrid Models: Best of Both Worlds.*