iSyncTab is a multimodal learning framework for image-tabular data that learns how heterogeneous features should be sequenced before multimodal fusion.
The framework treats both image and tabular representations as tokens and introduces Neural Synchrony-guided Paired Feature Sequencing (NS-PFS) to learn a coherent cross-modal ordering.
NS-PFS performs modality-specific clustering and aligns image and tabular feature clusters using neural synchrony based on feature energy and centroid similarity. Cross-modal cluster pairing is obtained through Hungarian matching, followed by construction of a synchronized global feature sequence.
The ordered representation is processed by an Order-aware Memory-augmented Transformer (OMT) with a Linformer backbone, learnable memory tokens, and a feature-sequencing consistency objective.
🧠 Core idea: image and tabular features are represented as tokens, synchronized through NS-PFS, and processed in the learned order by OMT.
The public checkpoint contains the model state, optimizer state, selected hyperparameters, preprocessing information, feature configuration, class mappings, dataset splits, and reproducibility metadata.
Stored training and test results are intentionally excluded from the public release.
🔐 File Integrity
The two PyTorch artifacts were verified locally and again after downloading them from the Hugging Face Model Hub.
Philipp Tschandl, Cliff Rosendahl, and Harald Kittler The HAM10000 Dataset, A Large Collection of Multi-Source Dermatoscopic Images of Common Pigmented Skin Lesions
Scientific Data, 2018 https://doi.org/10.1038/sdata.2018.161
No HAM10000 images, metadata tables, or other original dataset files are redistributed through this repository.
Users should obtain the dataset separately from the original distribution source and comply with the applicable dataset license and terms.
♻️ Reproducibility
The public release preserves the information required to reproduce the HAM10000 experiment setup:
selected model hyperparameters
NS-PFS configuration
feature definitions
categorical vocabularies
class mappings
image preprocessing
train, validation, and test split indices
random seeds
optimizer state
model state
The public split contains:
Split
Samples
Training
6,410
Validation
1,602
Test
2,003
Total
10,015
Stored experimental performance results are intentionally excluded from the public artifacts.
Users can train and evaluate iSyncTab using the released implementation and reproducible data splits to obtain the results independently.
🧪 Public Release Policy
The public release does not include stored:
test accuracy
test loss
training history
validation objective
Optuna trial results
local database paths
local device information
The release focuses on the trained model artifacts, configuration, reproducibility information, and source code needed to independently train and evaluate iSyncTab.
📓 Demo and Source Code
The complete implementation, experiment notebooks, and package demonstration are available in the official GitHub repository:
The notebook provides examples for package installation, HAM10000 experiments, generalized image-tabular usage, and loading trained model artifacts.
🎯 Intended Use
This release is intended for:
academic research
reproducibility studies
multimodal machine learning
image-tabular learning
feature sequencing research
medical AI research
non-commercial experimentation
⚕️ Medical Disclaimer
This model is a research artifact.
It is not a medical device and is not intended for clinical diagnosis, treatment recommendations, patient management, or direct clinical decision-making.
Any potential clinical application requires appropriate independent validation, safety assessment, ethical review, and regulatory approval.
📜 License
Model Artifacts
The trained HAM10000 model artifacts in this repository are released under:
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International
CC BY-NC-SA 4.0
Source Code
The iSyncTab source code is distributed separately under the MIT License:
The HAM10000 dataset itself is not redistributed through this model repository.
📖 Citation
If you use iSyncTab in your research, please cite:
bibtex
1@inproceedings{habib2026isynctab,
2 title = {iSyncTab: Learning Cross-Modal Feature Sequencing for Image-Tabular Data via Neural Synchrony},
3 author = {Habib, Al Zadid Sultan Bin and Ahamed, Md Younus and Gyawali, Prashnna Kumar and Doretto, Gianfranco and Adjeroh, Donald A.},
4 booktitle = {Proceedings of the European Conference on Computer Vision},
5 year = {2026},
6 doi = {10.1007/978-3-032-37035-8}
7}
Please also cite the original HAM10000 publication when using the HAM10000-trained artifacts.
👥 Authors
Al Zadid Sultan Bin Habib Md Younus Ahamed Prashnna Kumar Gyawali Gianfranco Doretto Donald A. Adjeroh
