Tootfinder

Automatic detection of abnormal clinical EEG: comparison of a finetuned foundation model with two deep learning models
Aurore Bussalb, Fran\c{c}ois Le Gac, Guillaume Jubien, Mohamed Rahmouni, Ruggero G. Bettinardi, Pedro Marinho R. de Oliveira, Phillipe Derambure, Nicolas Gaspard, Jacques Jonas, Louis Maillard, Laurent Vercueil, Herv\'e Vespignani, Philippe Laval, Laurent Koessler, Ulysse Gimenez

Automatic detection of abnormal clinical EEG: comparison of a finetuned foundation model with two deep learning models
Electroencephalography (EEG) is commonly used by physicians for the diagnosis of numerous neurological disorders. Due to the large volume of EEGs requiring interpretation and the specific expertise involved, artificial intelligence-based tools are being developed to assist in their visual analysis. In this paper, we compare two deep learning models (CNN-LSTM and Transformer-based) with BioSerenity-E1, a recently proposed foundation model, in the task of classifying entire EEG recordings as norm…

Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention
Syed Haider Ali, Asrar Ahmad, Muhammad Ali, Asifullah Khan, Muhammad Shahban, Nadeem Shaukat
https://arxiv.org/abs/2506.15562

Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention
Cancer is an abnormal growth with potential to invade locally and metastasize to distant organs. Accurate auto-segmentation of the tumor and surrounding normal tissues is required for radiotherapy treatment plan optimization. Recent AI-based segmentation models are generally trained on large public datasets, which lack the heterogeneity of local patient populations. While these studies advance AI-based medical image segmentation, research on local datasets is necessary to develop and integrate …

Mapping Brain-Behavior Correlations in Autism Using Heat Kernel Smoothing
Moo K. Chung, Kim M. Dalton, Daniel J. Kelley, Richard J. Davidson
https://arxiv.org/abs/2507.00473

Mapping Brain-Behavior Correlations in Autism Using Heat Kernel Smoothing
This paper presents a streamlined image analysis framework for correlating behavioral measures to anatomical measures on the cortex and detecting the regions of abnormal brain-behavior correlates. We correlated a facial emotion discrimination task score and its response time to cortical thickness measurements in a group of high functioning autistic subjects. Many previous correlation studies in brain imaging neglect to account for unwanted age effect and other variables and the subsequent stati…