XzhLoongSkills/skills/research-skills/medical-imaging-review/references/DOMAINS.md

Domain-Specific Method Taxonomies (3-Axis Grouping)

This file replaces the v2 "flat 10-subsection list" approach. For each medical imaging domain, methods are organized along 3 thematic axes, not as a long flat list. These axes are subsection structure (the H3 organization in §Methods), with bold lead-ins for individual method families inside each axis.

The 3 axes are universal:

1. Architectural priors — what kind of network (CNN, Transformer, Mamba, etc.)
2. Inductive priors — what kind of geometric / structural / multi-task bias is built in (topology, multi-task, graph, etc.)
3. Data regime — how data is used / pre-trained / federated (self-supervised, foundation models, federated, etc.)

Critical: These 3 axes are how the §Methods section is structured. Do NOT bullet 10 method categories as 10 H3 subsections. Use 3 axis subsections, with bold lead-ins inside each axis for the method families.

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How to use this file

When writing the §Methods section:

1. Open this file to find your domain.
2. Use the 3-axis grouping as your H3 structure.
3. Inside each axis subsection, use bold lead-ins for the individual method families.
4. End each axis subsection with a verdict sentence.

Example for coronary segmentation §Methods:

## Methods

### Architectural priors

**CNN-based design.** ... 2-3 paragraphs ...
**Transformer-based design.** ... 2-3 paragraphs ...
**Mamba and state-space design.** ... 1-2 paragraphs ...

Verdict: CNN-based design remains the operational default; transformer
hybrids are starting to show convincing gains in centerline-vs-mask hybrid
problems but have yet to displace U-Net on pure segmentation.

### Inductive priors

**Topology-aware design.** ... 2-3 paragraphs ...
**Multi-task design.** ... 2-3 paragraphs ...
**Graph neural network design.** ... 1-2 paragraphs ...

Verdict: Topology-aware losses are the single most cost-effective design
choice for coronary segmentation when paired with any decent backbone.

### Data regime

**Self-supervised pre-training.** ... 2-3 paragraphs ...
**Foundation models.** ... 2-3 paragraphs ...
**Federated learning.** ... 1-2 paragraphs ...
**Physics-informed models.** ... 1 paragraph ...

Verdict: Foundation models are the next 2-3 years' wild card; their gap to
domain-tuned specialists has narrowed substantially but is not yet closed.

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Coronary Artery / Cardiovascular CT (CCTA)

Axis 1: Architectural priors

• CNN-based: U-Net, V-Net, nnU-Net variants
• Transformer-based: ViT, SwinUNETR, TransUNet, TransCC, FocusUNETR
• Mamba / state-space: VM-UNet, U-Mamba for vessels
• Hybrid CNN-Transformer: nnFormer, hybrid encoders

Axis 2: Inductive priors

• Topology-aware: clDice loss, VCP loss, persistent-homology losses
• Multi-task: joint segmentation + centerline, joint segmentation + bifurcation detection
• Graph neural network: vessel graph extraction, GNN-based labeling

Axis 3: Data regime

• Self-supervised pre-training: contrastive, masked-image-modeling for vessels
• Foundation models: SAM-Med, vesselFM, generalist segmentation models
• Semi-supervised: pseudo-labeling for CCTA datasets
• Federated learning: multi-center coronary segmentation without data sharing
• Physics-informed: PDE-constrained losses for vessel topology

Downstream tasks (separate section, not part of methods axis structure)

• Centerline extraction
• Vessel labeling (SCCT 18-segment / AHA 17-segment myocardium)
• Stenosis detection
• CT-FFR computation
• Plaque analysis
• Calcium scoring
• Pericoronary fat analysis (FAI)

Key datasets

• CAT08 (32 cases, centerline)
• ASOCA (40 cases, segmentation)
• ImageCAS (1000 cases, single-center, segmentation)
• PCCTA120 (120 cases, artery + plaque)

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Lung Imaging (CT / X-ray)

Axis 1: Architectural priors

• Anchor-based detection: Faster R-CNN, RetinaNet
• Anchor-free detection: CenterNet, FCOS, YOLO variants
• Transformer-based detection: DETR family
• 3D detection: 3D nodule detection networks
• U-Net variants for segmentation

Axis 2: Inductive priors

• Multi-scale feature pyramids: FPN-based, PSP
• Attention mechanisms: SE blocks, CBAM, axial attention
• Boundary-aware: edge-loss formulations
• Uncertainty quantification: MC dropout, ensembles

Axis 3: Data regime

• Self-supervised: contrastive learning on chest CT
• Weakly-supervised: from radiology reports
• Foundation models: chest X-ray foundation models

Tasks

• Nodule detection / segmentation / malignancy classification
• COVID-19 detection
• Interstitial lung disease characterization

Key datasets

• LUNA16 (888 CT scans)
• LIDC-IDRI (1018 cases)
• ChestX-ray14 (112,120 X-rays)

---

Brain Imaging (MRI / CT)

Axis 1: Architectural priors

• CNN-based: U-Net, V-Net
• Transformer-based: SwinUNETR, UNETR for BraTS
• Hybrid

Axis 2: Inductive priors

• Attention mechanisms: spatial, channel, self-attention
• Graph neural networks: brain connectivity GNNs
• Multi-atlas-informed: deep atlas registration

Axis 3: Data regime

• Self-supervised pre-training: masked-image-modeling on MRI
• Foundation models: medSAM, BrainSAM
• Multi-modal fusion: T1/T2/FLAIR fusion strategies
• Federated learning: cross-institutional MRI federation

Tasks

• Brain tissue segmentation
• Tumor segmentation (BraTS)
• Lesion detection (stroke, MS)
• Cerebrovascular segmentation
• Age / disease estimation

Key datasets

• BraTS (brain tumor)
• ADNI (Alzheimer's)
• IXI (healthy brains)
• ISLES (stroke lesions)

---

Cardiac Imaging (MRI / CT / Echo)

Axis 1: Architectural priors

• CNN-based: nnU-Net, V-Net cine MRI
• Temporal modeling: RNN, 3D CNN, transformer-based temporal
• Multi-view fusion: SA + LA cine fusion

Axis 2: Inductive priors

• Shape priors: SSM-constrained networks
• Anatomical loss formulations
• Uncertainty estimation: ensembles, MC dropout

Axis 3: Data regime

• Multi-modal fusion: cine + LGE + perfusion
• Foundation models: cardiac generalists
• Self-supervised pre-training

Tasks

• Chamber segmentation
• Wall motion analysis
• Scar / fibrosis detection (LGE)
• Valve assessment
• Strain analysis

Key datasets

• ACDC (100 patients)
• M&Ms (320 subjects)
• CAMUS (500 patients, echo)

---

Pathology (Whole Slide Images)

Axis 1: Architectural priors

• Patch-based CNN: ResNet, EfficientNet
• Transformer-based: ViT, hierarchical transformers
• Graph neural networks: nuclei-level graphs

Axis 2: Inductive priors

• Multiple Instance Learning (MIL): attention-MIL, max-pooling
• Attention-based aggregation: TransMIL
• Topology-aware: persistent homology of histological structures

Axis 3: Data regime

• Self-supervised pre-training: SimCLR / MoCo / DINO on patches
• Foundation models: PathLM, CONCH, UNI, Virchow
• Weakly-supervised: from slide-level labels

Tasks

• Cancer detection / grading / staging
• Biomarker prediction
• Survival prediction

Key datasets

• CAMELYON (lymph node)
• TCGA (multi-cancer)
• PANDA (prostate)

---

Retinal Imaging (Fundus / OCT)

Axis 1: Architectural priors

• CNN-based: multi-scale networks
• Transformer-based: ViT for fundus
• Hybrid

Axis 2: Inductive priors

• Attention mechanisms: dual-attention for vessels
• Domain adaptation: between fundus camera types

Axis 3: Data regime

• Self-supervised: on large unlabeled fundus image sets
• Foundation models: RETFound, FLAIR
• Federated learning: privacy-preserving DR screening

Tasks

• Diabetic retinopathy grading
• Glaucoma detection
• Age-related macular degeneration
• Vessel segmentation

Key datasets

• EyePACS (88,702 images)
• DRIVE (40 images, vessels)
• REFUGE (1200 images, glaucoma)

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Universal Medical Image Segmentation (Fallback Axis Structure)

When the domain is generic or your topic spans multiple modalities, use this universal 3-axis grouping:

Axis 1: Architectural priors

• Encoder-Decoder (U-Net, V-Net, nnU-Net)
• Transformer-based (SwinUNETR, UNETR, TransUNet)
• Mamba / state-space
• Hybrid CNN-Transformer

Axis 2: Inductive priors

• Attention mechanisms (SE, CBAM, axial, deformable)
• Multi-scale processing (FPN, PSP, ASPP)
• Boundary-aware (active contours, edge losses)
• Topology-preserving (clDice, persistent homology)
• Uncertainty quantification (MC Dropout, ensembles)

Axis 3: Data regime

• Self-supervised pre-training (contrastive, masked)
• Foundation models (SAM, MedSAM)
• Few-shot / zero-shot (prototypical, foundation models)
• Domain adaptation (adversarial, self-training)
• Federated learning
• Efficient architectures (MobileNet, EfficientNet, Mamba — when efficiency is the focus)

Universal evaluation metrics (Box 1 content)

• Overlap: Dice, IoU / Jaccard
• Distance: Hausdorff (HD, HD95), ASSD
• Topology: clDice, Betti numbers
• Clinical: Sensitivity, Specificity, AUC, PPV / NPV

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What to do when a method family doesn't fit cleanly into 3 axes

Some methods span axes (e.g., a foundation-model-based topology-aware Mamba would touch all 3). In such cases:

• Place the method in the axis it's most centrally about.
• Cross-reference from the other 2 axes ("see also: this method type combines architectural and data-regime innovations").
• Don't create a 4th axis to accommodate it. Three axes is the structural commitment.

If 30%+ of your methods don't fit, you may be in a sub-domain where the 3-axis structure needs adaptation. In that case, document the alternative structure in PARADIGM.md and use it consistently — but stay disciplined to 3 axes.

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Why 3 axes, not 10 flat subsections

The v2 skill's coronary section listed 10 flat method categories. The resulting draft had a §3 with 10 nearly-equal H3 subsections, each ~500 words. The effect on the reader: a textbook chapter, not a flagship review.

Flagship reviews compress 10+ method variants into 3 thematic axes. The 3-axis structure also forces explicit comparison ("CNN-based vs Transformer-based vs Mamba-based architectures all aim to capture spatial inductive bias differently") which is what a real review reader wants — synthesis, not catalogue.

If your editor / reviewer feedback says "the methods section reads as a flat list", this file's structure is the fix.