# 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. --- ## 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: ```markdown ## 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. ``` --- ## 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) --- ## 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) --- ## 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 --- ## 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. --- ## 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.