Deep learning-based auditory attention decoding in listeners with hearing impairment.

Objective.This study develops a deep learning (DL) method for fast auditory attention decoding (AAD) using electroencephalography (EEG) from listeners with hearing impairment (HI). It addresses three classification tasks: differentiating noise from speech-in-noise, classifying the direction of attended speech (left vs. right) and identifying the activation status of hearing aid noise reduction algorithms (OFF vs. ON). These tasks contribute to our understanding of how hearing technology influences auditory processing in the hearing-impaired population.Approach.Deep convolutional neural network (DCNN) models were designed for each task. Two training strategies were employed to clarify the impact of data splitting on AAD tasks: inter-trial, where the testing set used classification windows from trials that the training set had not seen, and intra-trial, where the testing set used unseen classification windows from trials where other segments were seen during training. The models were evaluated on EEG data from 31 participants with HI, listening to competing talkers amidst background noise.Main results.Using 1 s classification windows, DCNN models achieve accuracy (ACC) of 69.8%, 73.3% and 82.9% and area-under-curve (AUC) of 77.2%, 80.6% and 92.1% for the three tasks respectively on inter-trial strategy. In the intra-trial strategy, they achieved ACC of 87.9%, 80.1% and 97.5%, along with AUC of 94.6%, 89.1%, and 99.8%. Our DCNN models show good performance on short 1 s EEG samples, making them suitable for real-world applications. Conclusion: Our DCNN models successfully addressed three tasks with short 1 s EEG windows from participants with HI, showcasing their potential. While the inter-trial strategy demonstrated promise for assessing AAD, the intra-trial approach yielded inflated results, underscoring the important role of proper data splitting in EEG-based AAD tasks.Significance.Our findings showcase the promising potential of EEG-based tools for assessing auditory attention in clinical contexts and advancing hearing technology, while also promoting further exploration of alternative DL architectures and their potential constraints.

Convolutional neural networks ensemble model for neonatal seizure detection.

BACKGROUND: Neonatal seizures are a common occurrence in clinical settings, requiring immediate attention and detection. Previous studies have proposed using manual feature extraction coupled with machine learning, or deep learning to classify between seizure and non-seizure states. NEW METHOD: In this paper a deep learning based approach is used for neonatal seizure classification using electroencephalogram (EEG) signals. The architecture detects seizure activity in raw EEG signals as opposed to common state-of-art, where manual feature extraction with machine learning algorithms is used. The architecture is a two-dimensional (2D) convolutional neural network (CNN) to classify between seizure/non-seizure states. RESULTS: The dataset used for this study is annotated by three experts and as such three separate models are trained on individual annotations, resulting in average accuracies (ACC) of 95.6 %, 94.8 % and 90.1 % respectively, and average area under the receiver operating characteristic curve (AUC) of 99.2 %, 98.4 % and 96.7 % respectively. The testing was done using 10-cross fold validation, so that the performance can be an accurate representation of the architectures classification capability in a clinical setting. After training/testing of the three individual models, a final ensemble model is made consisting of the three models. The ensemble model gives an average ACC and AUC of 96.3 % and 99.3 % respectively. COMPARISON WITH EXISTING METHODS: This study outperforms previous studies, with increased ACC and AUC results coupled with use of small time windows (1 s) used for evaluation. CONCLUSION: The proposed approach is promising for detecting seizure activity in unseen neonate data in a clinical setting.

Cancer Image Quantification With And Without, Expensive Whole Slide Imaging Scanners.

Automated analysis of digitized pathology images in tele-health applications can have a transformative impact on under-served communities in the developing world. However, the vast majority of existing image analysis algorithms are trained on slide images acquired via expensive Whole-Slide-Imaging (WSI) scanners. High scanner cost is a key bottleneck preventing large-scale adoption of digital pathology in developing countries. In this work, we investigate the viability of automated analysis of slide images captured from the eyepiece of a microscope via a smart phone. The mitosis detection application is considered as a use case.Results indicate performance degradation when using (lower-quality) smartphone images; as expected. However, the performance gap is not too wide (F1-score smartphone=0.65, F1-score WSI=0.70) demonstrating that smartphones could potentially be employed as image acquisition devices for digital pathology at locations where expensive scanners are not available.

Skin Lesion Classification Using GAN based Data Augmentation.

Early detection and frequent monitoring are critical for survival of skin cancer patients. Unfortunately, in practice a significant number of cases remain undetected until advanced stages, reducing the chances of survival. An appealing approach for early detection is to employ automated classification of dermoscopic images acquired via low-cost, smartphone-based hardware. By far, the most successful classification approaches on this task are based on deep learning. Unfortunately, most medical image classification tasks are unable to leverage the true potential of deep learning due to limited sizes of training datasets. Investigation of novel data generation techniques is thus an appealing option since it can enable us to augment our training data by a large number of synthetically generated examples. In this work, we investigate the possibility of obtaining realistic looking dermoscopic images via generative adversarial networks (GANs). These images are then employed to augment our existing training set in an effort to enhance the performance of a deep convolutional neural network on the skin lesion classification task. Results are compared with conventional data augmentation strategies and demonstrate that GAN based augmentation delivers significant performance gains.