Psychiatric disorders are among the most common and debilitating illnesses across the lifespan and begin usually during childhood and adolescence, which emphasizes the importance of studying the developing brain. Most of the previous pediatric neuroimaging studies employed traditional univariate statistics on relatively small samples. Multivariate machine learning approaches have a great potential to overcome the limitations of these approaches. On the other hand, the vast majority of existing multivariate machine learning studies have focused on differentiating between children with an isolated psychiatric disorder and typically developing children. However, this line of research does not reflect the real-life situation as the majority of children with a clinical diagnosis have multiple psychiatric disorders (multimorbidity), and consequently, a clinician has the task to choose between different diagnoses and/or the combination of multiple diagnoses. Thus, the goal of the present benchmark is to predict psychiatric multimorbidity in children and adolescents. For this purpose, we implemented two kinds of machine learning benchmark challenges: The first challenge targets the prediction of the seven most prevalent DSM-V psychiatric diagnoses for the available data set, of which each individual can exhibit multiple ones concurrently (i.e. multi-task multi-label classification). Based on behavioral and cognitive measures, a second challenge focuses on predicting psychiatric symptom severity on a dimensional level (i.e. multiple regression task). For the present benchmark challenges, we will leverage existing and future data from the biobank of the Healthy Brain Network (HBN) initiative, which offers a unique large-sample dataset (N = 2042) that provides a wide array of different psychiatric developmental disorders and true hidden data sets. Due to limited real-world practicability and economic viability of MRI measurements, the present challenge will permit only resting state EEG data and demographic information to derive predictive models. We believe that a community driven effort to derive predictive markers from these data using advanced machine learning algorithms can help to improve the diagnosis of psychiatric developmental disorders.
@article{langer2022benchmark,title={A benchmark for prediction of psychiatric multimorbidity from resting EEG data in a large pediatric sample},author={Langer, Nicolas and Plomecka, Martyna Beata and Tr{\"o}ndle, Marius and Negi, Anuja and Popov, Tzvetan and Milham, Michael and Haufe, Stefan},journal={NeuroImage},pages={119348},year={2022},publisher={Elsevier},}
2020
RDA-UNET-WGAN: an accurate breast ultrasound lesion segmentation using wasserstein generative adversarial networks
Anuja Negi, Alex Noel Joseph Raj, Ruban Nersisson, Zhemin Zhuang, and
1 more author
Early-stage detection of lesions is the best possible way to fight breast cancer, a disease with the highest malignancy ratio among women. Though several methods primarily based on deep learning have been proposed for tumor segmentation, it is still a challenging problem due to false positives and the precise boundary detection required for segmentation. In this paper, we propose a Generative Adversarial Network (GAN) based algorithm for segmenting the tumor in Breast Ultrasound images. The GAN model comprises of two modules: generator and discriminator. Residual-Dilated-Attention-Gate-UNet (RDAU-NET) is used as the generator which serves as a segmentation module and a CNN classifier is employed as the discriminator. To stabilize training, Wasserstein GAN (WGAN) algorithm has been used. The proposed hybrid deep learning model is called the WGAN-RDA-UNET. The model is assessed with several quantitative metrics and is also compared with existing methods both quantitatively and qualitatively. The overall Accuracy, PR-AUC, ROC-AUC and F1-score achieved were 0.98, 0.95, 0.89 and 0.88 respectively which are better than most conventional deep net models. The results also showcase the shortcomings of CNN, RDA U-Net and other models and how they can be rectified using the WGAN-RDA-UNET model.
@article{negi2020rda,title={RDA-UNET-WGAN: an accurate breast ultrasound lesion segmentation using wasserstein generative adversarial networks},author={Negi, Anuja and Raj, Alex Noel Joseph and Nersisson, Ruban and Zhuang, Zhemin and Murugappan, M},journal={Arabian Journal for Science and Engineering},volume={45},number={8},pages={6399--6410},year={2020},publisher={Springer},}
2018
Eye state detection for use in advanced driver assistance systems
Shubham Sharma, Anuja Negi, Shantanu Singh, Dinesh Samuel Sathia Raj, and
3 more authors
In International Conference on Recent Trends in Advance Computing (ICRTAC) 2018
Most automobiles lack reliable smart systems that can constantly track the driver’s behaviour and raise alarms as required. Extant systems are either too slow or not robust enough to cope with different types of drivers and conditions. In this paper, a robust system to continuously track the driver’s eye and detect its state (open/close) is proposed. Frames from a live camera feed are constantly processed. Viola Jones algorithm, using Haar filters extracts the eye. The extraction is efficient with and without spectacles (translucent) and the system can even estimate the Region of Interest (RoI) where it is most likely to find the eye in the event that no eyes are explicitly detected. A trained CNN model using the LeNet architecture classifies the extracted eyes. The rate at which predictions are made is also higher than existing systems. The system raises an alarm if, after analysing the data points, it detects any anomalies.
@inproceedings{sharma2018eye,title={Eye state detection for use in advanced driver assistance systems},author={Sharma, Shubham and Negi, Anuja and Singh, Shantanu and Raj, Dinesh Samuel Sathia and Graceline, Jasmine S and Vaidehi, V and Ganesan, Subramaniam},booktitle={International Conference on Recent Trends in Advance Computing (ICRTAC)},pages={155--161},year={2018},organization={IEEE},}
Gini Index and Entropy-Based Evaluation: A Retrospective Study and Proposal of Evaluation Method for Image Segmentation
Anuja Negi, Shubham Sharma, and J Priyadarshini
In International Conference on Nanoelectronics, Circuits and Communication Systems 2018
Image segmentation is an old and ever-growing field in computer vision. Several image segmentation algorithms with diverse approach methodologies have been proposed over the years. As a result, several evaluation criteria have also been proposed. Many of these segmentation and evaluation methods are based on the famous Gini index. Several have tried using entropy values as well. Methods based on these have been tuned and modified for betterment over the years. This paper does a thorough literature survey on the growth and usage of Gini index and entropy for segmentation and primarily evaluation. Realizing the potential as well as the limitations, the paper proposes an evaluation criteria based on Gini index and entropy. The proposed algorithm uses the concept of maximum intra-region homogeneity and inter-region heterogeneity in segments to evaluate a segmentation technique. Evaluation is done on segments as seen by the segmentation technique in the original input image.
@inproceedings{negi2018gini,title={Gini Index and Entropy-Based Evaluation: A Retrospective Study and Proposal of Evaluation Method for Image Segmentation},author={Negi, Anuja and Sharma, Shubham and Priyadarshini, J},booktitle={International Conference on Nanoelectronics, Circuits and Communication Systems},pages={239--248},year={2018},organization={Springer},}
