GraPHFormer: A Multimodal Graph Persistent Homology Transformer for the Analysis of Neuroscience Morphologies

CVPR 2026 (Main Track)

Uzair Shah, Marco Agus, Mahmoud Gamal, Mahmood Alzubaidi, Corrado Cali, Pierre J. Magistretti, Abdesselam Bouzerdoum, Mowafa Househ

[Paper]

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Overview

GraPHFormer is a multimodal self-supervised framework for neuronal morphology analysis that jointly models topological and structural information from neuron reconstructions. It combines:

• Vision branch: A three-channel persistence image (unweighted, persistence-weighted, and radius-weighted topological densities) processed by a frozen DINOv2-ViT-S backbone.
• Graph branch: A TreeLSTM encoder that captures geometric and radial attributes from the morphological skeleton graph.

The two branches are aligned in a shared embedding space using CLIP-style contrastive learning with a symmetric InfoNCE loss. Persistence-space augmentations are used during training to maintain topological meaning across views.

GraPHFormer achieves state-of-the-art performance on five of six neuronal morphology benchmarks spanning both self-supervised and supervised settings.

Repository Structure

GraPHFormer/
├── train.py                          # Self-supervised pretraining
├── finetune.py                       # Supervised fine-tuning
├── setup.py
├── scripts/
│   └── prepare_data.py               # Data preprocessing pipeline
└── graphformer/
    ├── models/
    │   ├── clip_model.py             # CLIP-style dual-branch model
    │   ├── image_encoder.py          # DINOv2 / ResNet image encoders
    │   ├── tree_encoder.py           # TreeLSTM graph encoder
    │   ├── fusion.py                 # Multimodal fusion heads
    │   └── finetune_model.py         # Fine-tuning wrapper
    ├── data/
    │   ├── dataset.py                # NeuronTreeDataset
    │   └── persistence_image.py      # Persistence image computation
    ├── losses/
    │   ├── infonce.py
    │   └── contrastive.py
    └── augmentations/
        ├── tree_augmentations.py     # Graph-space augmentations
        └── persistence_augmentations.py  # Topology-preserving image augmentations

Installation

pip install -e .

Dependencies: Python >= 3.8, PyTorch >= 1.10, torchvision >= 0.11, DGL >= 0.8, scikit-learn, networkx, nltk, Pillow, tqdm, numpy.

Data Preparation

Dataset downloading and preprocessing follow the TreeMoCo paper (NeurIPS 2022). Please refer to the TreeMoCo GitHub repository for instructions on downloading the three datasets: BIL (Brain Image Library), ACT (Allen Cell Types), and JML (Janelia MouseLight).

Once the raw SWC files are in place, run the preprocessing script:

python scripts/prepare_data.py

This normalizes soma position/orientation/scale, removes axon compartments, computes branch-level features, and creates 10-fold cross-validation splits.

Training

Self-Supervised Pretraining

python train.py \
    --exp_name my_experiment \
    --dataset all_wo_others \
    --image_encoder dinov2_vits14 \
    --tree_model double \
    --embed_dim 128 \
    --batch_size 128 \
    --epochs 100 \
    --lr 3e-4 \
    --use_knn_eval \
    --eval_jm --eval_act

Key options:

Argument	Default	Description
--image_encoder	resnet18	dinov2_vits14, resnet18, resnet50, persistencevit
--tree_model	double	TreeLSTM variant: ori, v2, double
--embed_dim	128	Shared embedding dimension
--loss_type	clip	clip, infonce, ntxent, triplet
--use_persistence_aug	off	Enable persistence-space augmentations
--knn_fusion	concat	How to combine modalities for KNN eval

Tree augmentations: --aug_rotate, --aug_flip, --aug_jitter_coords, --aug_drop_tree, --aug_skip_parent_node, --aug_swap_sibling_subtrees

Fine-Tuning

python finetune.py \
    --exp_name my_finetune \
    --pretrained_checkpoint work_dir/my_experiment/best_BIL.pth \
    --dataset bil_6_classes \
    --mode multimodal \
    --fusion_mode concat \
    --epochs 50 \
    --lr 1e-4

Fine-tuning modes: multimodal, image_only, tree_only. Fusion modes: concat, add, cross_attention, gated, cmf, mhcma.

Two-stage training (linear probe then full fine-tune):

python finetune.py \
    --exp_name my_finetune \
    --pretrained_checkpoint work_dir/my_experiment/best_BIL.pth \
    --dataset bil_6_classes \
    --linear_probe_epochs 10 \
    --epochs 50

Benchmarks

Evaluated on six datasets:

Dataset	Task	Classes
BIL-6	Brain region classification	6
ACT-4	Cortical layer classification	4
JML-4	Brain region classification	4
N7	Neuron type classification	7
M1-Cell	Cell type classification	—
M1-REG	Cortical region classification	—

GraPHFormer achieves state-of-the-art on 5/6 benchmarks, outperforming topology-only, graph-only, and morphometrics baselines.

Citation

@inproceedings{shah2026graphformer,
  title     = {GraPHFormer: A Multimodal Graph Persistent Homology Transformer for the Analysis of Neuroscience Morphologies},
  author    = {Shah, Uzair and Agus, Marco and Gamal, Mahmoud and Alzubaidi, Mahmood and Cali, Corrado and Magistretti, Pierre J. and Bouzerdoum, Abdesselam and Househ, Mowafa},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (Main Track)},
  year      = {2026}
}