Convolutional Neural Network for Stereotypical Motor Movement Detection in Autism

no code implementations • 5 Nov 2015 • Nastaran Mohammadian Rad, Andrea Bizzego, Seyed Mostafa Kia, Giuseppe Jurman, Paola Venuti, Cesare Furlanello

Autism Spectrum Disorders (ASDs) are often associated with specific atypical postural or motor behaviors, of which Stereotypical Motor Movements (SMMs) have a specific visibility.

Transfer Learning valid

Deep Learning for Automatic Stereotypical Motor Movement Detection using Wearable Sensors in Autism Spectrum Disorders

no code implementations • 14 Sep 2017 • Nastaran Mohammadian Rad, Seyed Mostafa Kia, Calogero Zarbo, Twan van Laarhoven, Giuseppe Jurman, Paola Venuti, Elena Marchiori, Cesare Furlanello

Our results show that: 1) feature learning outperforms handcrafted features; 2) parameter transfer learning is beneficial in longitudinal settings; 3) using LSTM to learn the temporal dynamic of signals enhances the detection rate especially for skewed training data; 4) an ensemble of LSTMs provides more accurate and stable detectors.

Ensemble Learning Transfer Learning

Hybrid Deep Neural Network for Brachial Plexus Nerve Segmentation in Ultrasound Images

no code implementations • 1 Jun 2021 • Juul P. A. van Boxtel, Vincent R. J. Vousten, Josien Pluim, Nastaran Mohammadian Rad

In this paper, we propose a hybrid model consisting of a classification model followed by a segmentation model to segment BP nerve regions in ultrasound images.

Segmentation

PROMISSING: Pruning Missing Values in Neural Networks

no code implementations • 3 Jun 2022 • Seyed Mostafa Kia, Nastaran Mohammadian Rad, Daniel van Opstal, Bart van Schie, Andre F. Marquand, Josien Pluim, Wiepke Cahn, Hugo G. Schnack

In this method, there is no need to remove or impute the missing values; instead, the missing values are treated as a new source of information (representing what we do not know).

BIG-bench Machine Learning Imputation