Papers Using VisualTorch

Published research that has used VisualTorch to visualize model architectures. Used it in your own research, or know of a paper that cites it - even if it’s not yours? Open a pull request to add it here, or tell us about it and we’ll add it for you.

Paper	Venue (Year)
Deep learning-based high-information-content graph representation of early stage bacterial biofilms	npj Biofilms and Microbiomes (2026)
Energy-Efficient Epileptic Seizure Prediction Using Spiking Neural Networks	IEEE ISCAS (2026)
Failure Evaluation of Steel Plate Shear Walls in Multi-Storey Steel Buildings Under Seismic Excitation Using Convolutional Neural Networks	Materials, MDPI (2026)
Kolmogorov–Arnold neural network for identification of functional groups from FTIR spectra	Chemometrics and Intelligent Laboratory Systems (2025)

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Deep learning-based high-information-content graph representation of early stage bacterial biofilms

Authors: Nersesyan, L. E., Boiko, D. A., Kurbanalieva, S., Dzhemileva, L. U., Kozlov, K. S., Ananikov, V. P. (2026) Venue: npj Biofilms and Microbiomes Link: https://www.nature.com/articles/s41522-026-00971-3

Models early-stage bacterial biofilms as interaction graphs (cells as vertices, predicted intercellular interactions as edges), combining Mask R-CNN for cell segmentation with a custom network (BINet) for interaction prediction - enabling classification of developmental stage and substrate type from image-derived graph features.

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Energy-Efficient Epileptic Seizure Prediction Using Spiking Neural Networks

Authors: Brady, A., Moore-Hill, D., Khan, F., Daoud, H. (2026) Venue: IEEE International Symposium on Circuits and Systems (ISCAS) Link: https://ieeexplore.ieee.org/document/11562867

A patient-specific model, trained on the CHB-MIT scalp EEG dataset, that combines convolutional layers with Leaky Integrate-and-Fire spiking neurons and a recurrent network to detect pre-ictal (pre-seizure) brain states - the low energy consumption of spiking neurons targets power-constrained, on-device seizure prediction for wearable/IoT devices.

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Failure Evaluation of Steel Plate Shear Walls in Multi-Storey Steel Buildings Under Seismic Excitation Using Convolutional Neural Networks

Authors: Bonfini, P., Schetakis, N., Sukhnandan, J., Drosopoulos, G. A., Stavroulakis, G. E. (2026) Venue: Materials (MDPI) Link: https://www.mdpi.com/1996-1944/19/5/878

Trains a CNN on physics-based finite element simulations to predict equivalent plastic strain (failure distribution) on steel plate shear walls from building geometry and seismic intensity, for use in structural digital twins.

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Kolmogorov–Arnold neural network for identification of functional groups from FTIR spectra

Authors: Urbańczyk, T., Bożek, J., Mirczak, S., Koperski, J., Krośnicki, M. (2025) Venue: Chemometrics and Intelligent Laboratory Systems Link: https://www.sciencedirect.com/science/article/pii/S0169743925001066

Trains separate binary classifiers to identify 22 molecular functional groups from one-dimensional FTIR spectra. The study compares a classic CNN ending in fully connected linear layers with a CNN-KAN that replaces those final layers with Kolmogorov–Arnold layers, while both architectures share the same convolutional feature extractor.