Are General-Purpose Vision Models All We Need for 2D Medical Image Segmentation? A Cross-Dataset Empirical Study

This paper presents a cross-dataset empirical study demonstrating that general-purpose vision models often outperform specialized medical segmentation architectures in 2D medical image segmentation while also capturing clinically relevant structures, suggesting they are a viable alternative to domain-specific methods.

The Gist

Imagine you are trying to build a team of expert detectives to solve different types of mysteries.

For the last decade, the medical world has been hiring Specialist Detectives. These are experts trained specifically in one field: one only looks at skin moles, another only looks at heart ultrasound scans, and a third only looks at polyps in the colon. They use custom-made magnifying glasses and special tools designed just for their specific job. The belief was: "To find the tiny, tricky clues in medical images, you need a detective built from the ground up for that exact task."

But recently, a new type of detective has emerged: the Generalist Detective. These are super-smart agents trained on millions of photos of everyday life—cats, cars, trees, and street signs. They are incredibly good at spotting objects in anything they see.

The Big Question:
Do we still need to hire the expensive, custom-built Specialist Detectives for medical work? Or are the Generalist Detectives so smart that they can do the medical job just as well, if not better, without needing special training?

The Experiment: A Fair Race

The authors of this paper decided to settle this debate with a fair race. They didn't just look at old reports; they put the models in the same arena with the same rules.

• The Contestants: They picked 11 different models.
  • 5 Specialists: The "Medical" models (like U-Net, which is the classic medical detective, and some newer, fancy ones using advanced math).
  • 6 Generalists: The "Everyday" models (trained on natural photos but adapted for medical use).
• The Test Tracks: They ran these detectives through three very different medical challenges:
  1. Skin Lesions: Finding weird spots on skin (RGB color images).
  2. Colon Polyps: Finding growths inside the colon (RGB color images).
  3. Heart Chambers: Mapping the inside of a beating heart (Grayscale ultrasound images).

The Results: The Generalists Win!

Here is the plot twist: The Generalist Detectives won.

In almost every category, the models trained on everyday photos (General-Purpose Vision Models) outperformed the custom-built medical models.

• The Score: The Generalists achieved higher accuracy in finding the medical structures.
• The "Why": The researchers used a tool called Grad-CAM (think of it as a "heat map" that shows where the AI is looking). They found that the Generalists were looking at the right parts of the image. They could spot the clinically important details without ever being explicitly told, "Hey, this is a heart; look here." They just figured it out because they are so good at understanding shapes and patterns in general.

The Only Exception

There was one medical model that gave the Generalists a run for their money: Swin-UMamba. It was the only "Specialist" that could keep up with the Generalists, but even then, it didn't beat them.

What Does This Mean for the Future?

This paper suggests a major shift in how we build medical AI:

1. Don't Reinvent the Wheel: We might not need to spend years and millions of dollars inventing a new "Medical-Only" architecture for every single disease.
2. Use What Works: We can take these powerful, pre-trained Generalist models (which are already available and free) and fine-tune them for medical tasks.
3. Focus on the Real Problems: Instead of arguing over which mathematical formula is best for the AI's brain, researchers should focus on:
   • Getting better, cleaner data.
   • Making sure the AI works on patients it hasn't seen before.
   • Fixing the messy parts of real-world hospital data.

The Bottom Line

Think of it like this: If you need to fix a leaky pipe, you used to think you needed a plumber with a custom-made wrench. This study shows that a really smart, versatile handyman with a standard toolkit can actually fix the pipe just as well, maybe even better.

The takeaway: For 2D medical image segmentation, the "Generalist" models are likely all we need. We should stop obsessing over building new specialized tools and start using the powerful, general tools we already have.

Technical Summary

1. Problem Statement

Medical Image Segmentation (MIS) is critical for computer-assisted diagnosis and treatment planning. Historically, the field has relied on Specialized Medical Architectures (SMAs) (e.g., U-Net variants, Transformers adapted for medical data) designed to handle specific domain challenges like low contrast, small anatomical structures, and limited annotated data.

Simultaneously, General-Purpose Vision Models (GP-VMs) have advanced rapidly in natural image processing (e.g., semantic segmentation on ImageNet/ADE20K). While these models show strong generalization, their effectiveness in medical contexts remains debated. Previous comparisons are often flawed due to:

• Lack of standardized evaluation protocols.
• Inconsistent pre-processing and augmentation strategies.
• Reliance on metrics reported in original papers rather than controlled re-evaluation.

Core Question: Do MIS tasks truly require specialized architectures, or can modern GP-VMs achieve comparable or superior performance when evaluated under rigorous, standardized conditions?

2. Methodology

The authors conducted a controlled, cross-dataset empirical study to eliminate confounding variables.

A. Model Selection

The study benchmarked 11 architectures divided into two categories:

1. Specialized Medical Architectures (SMAs):
   • U-Net: The foundational baseline.
   • HiFormer-B: Hybrid CNN-ViT.
   • MISSFormer: Pure Transformer.
   • Swin-UMamba: Hybrid State-Space (Mamba) modeling.
   • U-KAN-L: Hybrid CNN + Kolmogorov-Arnold Networks (KAN).
2. General-Purpose Vision Models (GP-VMs):
   • Semantic Segmentation (SS) Architectures: SegFormer-B3, SegNeXt-L, VWFormer (with MiT-B3 and ConvNeXt-S backbones).
   • Vision Backbones (VB) adapted for SS: InternImage-T and TransNeXt-Tiny (using a UPerHead decoder).

B. Dataset Selection

Three heterogeneous datasets were used to test cross-domain robustness:

1. ISIC'18: RGB dermoscopy images (Binary lesion segmentation).
2. BKAI-IGH NeoPolyp: RGB endoscopy images (Multi-class polyp segmentation).
3. CAMUS: Grayscale echocardiography images (Multi-class cardiac region segmentation).

C. Standardized Training & Evaluation Protocol

To ensure fairness, all models were subjected to a unified protocol:

• Preprocessing: Identical augmentation pipelines and dataset-specific filtering (removing duplicates via perceptual hashing).
• Training:
  • Input resolution: 512 × 512.
  • Optimizer: AdamW with Reflected Exponential (REX) scheduler.
  • Initialization: ImageNet-pretrained encoders (except U-KAN, trained from scratch).
  • Hyperparameter Search: Systematic learning rate search for each model-dataset pair.
  • Validation: 5-fold cross-validation with patient-aware splitting for CAMUS to prevent data leakage.
• Metrics: Mean Intersection over Union (mIoU), Dice Similarity Coefficient (mDSC), Recall, and Precision (micro-averaged, excluding background).
• Explainability (XAI): Grad-CAM visualizations were generated to analyze model attention and clinical relevance.

3. Key Contributions

1. Comprehensive Empirical Study: A systematic comparison of 11 diverse architectures across three distinct medical imaging modalities (RGB binary, RGB multi-class, Grayscale multi-class).
2. Rigorous Benchmarking Framework: Establishment of a standardized protocol controlling for augmentation, training procedures, and data leakage, addressing the reproducibility crisis in medical AI.
3. Practical Insights & XAI Analysis: Demonstration that GP-VMs not only match but often exceed SMAs in accuracy, while also capturing clinically relevant structures in attention maps without domain-specific design.

4. Results

Segmentation Performance

• GP-VMs Outperform SMAs: Across all three datasets, the top-performing models were exclusively GP-VMs.
  • Top Performers: VW-MiT (91.0% avg mDSC), VW-Conv and TransNeXt (90.9%), InternImage (90.8%).
  • Best SMA: Swin-UMamba (SU-Mamba) achieved 90.5% avg mDSC, which was comparable but slightly lower than the top GP-VMs.
  • Other SMAs: Models like U-KAN, HiFormer, and U-Net trailed significantly (≤87.9% avg mDSC).
• Dataset Dependency:
  • NeoPolyp: The performance gap was largest here. GP-VMs achieved 88.7–89.6% mDSC, while most SMAs struggled (82.5–84.6%). Specifically, SMAs failed to reliably segment non-neoplastic polyps (Class C1).
  • ISIC'18 & CAMUS: The gap narrowed (1–2%), but GP-VMs remained superior or equal to the best SMAs.

Explainability (Grad-CAM)

• Clinical Relevance: GP-VMs consistently focused on clinically relevant regions. In difficult cases (e.g., ISIC'18 lesions), GP-VMs often provided more precise attention maps than SMAs.
• Failure Modes: All models struggled with specific classes (e.g., non-neoplastic polyps in NeoPolyp). Notably, some SMAs (HiFormer, U-KAN) showed no attention to these difficult classes, whereas GP-VMs maintained some level of focus.

5. Significance and Conclusion

Implications for Research

• Viability of GP-VMs: The study suggests that specialized medical architectures are not strictly necessary for 2D MIS. GP-VMs, pre-trained on massive natural image corpora, can serve as viable, often superior, alternatives.
• Resource Allocation: The authors argue that the research community should shift focus from incremental architectural innovation (designing new SMAs) toward:
  • Rigorous data curation.
  • Optimized training protocols.
  • Systematic evaluation of Out-of-Distribution (OOD) generalization.
• Model Selection: Practitioners should prioritize informed model selection, potentially leveraging existing GP-VMs to reduce computational and labor costs associated with developing novel architectures.

Limitations & Future Work

• Scope: Findings are limited to 2D imaging and the specific datasets/models tested. Generalization to 3D imaging or extremely low-data regimes is not guaranteed.
• Future Directions: The authors plan to extend the study to 3D modalities, assess OOD generalization more deeply, and release the benchmarking tool as an open-source resource to support the community.

Final Verdict: While specialized models still hold value for specific, niche clinical scenarios, General-Purpose Vision Models represent a powerful, often superior, baseline for 2D medical image segmentation, challenging the necessity of designing new domain-specific architectures for every task.