Instabilities granular media with flexible boundaries

by Debayan BhattacharyaPh.D

Tissue Classification During Needle Insertion Using Self-Supervised Contrastive Learning and Optical Coherence Tomography

Needle positioning is essential for various medical applications such as epidural anaesthesia. Physicians rely on their instincts while navigating the needle in epidural spaces. Thereby, identifying the tissue structures may be helpful to the physician as they can provide additional feedback in the needle insertion process. To this end, we propose a deep neural network that classifies the tissues from the phase and intensity data of complex OCT signals acquired at the needle tip. We investigate the performance of the deep neural network in a limited labelled dataset scenario and propose a novel contrastive pretraining strategy that learns invariant representation for phase and intensity data. We show that with 10% of the training set, our proposed pretraining strategy helps the model achieve an F1 score of 0.84 whereas the model achieves an F1 score of 0.60 without it. Further, we analyse the importance of phase and intensity individually towards tissue classification

Unsupervised Anomaly Detection of Paranasal Anomalies in the Maxillary Sinus

Deep learning (DL) algorithms can be used to automate paranasal anomaly detection from Magnetic Resonance Imaging (MRI). However, previous works relied on supervised learning techniques to distinguish between normal and abnormal samples. This method limits the type of anomalies that can be classified as the anomalies need to be present in the training data. Further, many data points from normal and anomaly class are needed for the model to achieve satisfactory classification performance. However, experienced clinicians can segregate between normal samples (healthy maxillary sinus) and anomalous samples (anomalous maxillary sinus) after looking at a few normal samples. We mimic the clinicians ability by learning the distribution of healthy maxillary sinuses using a 3D convolutional auto-encoder (cAE) and its variant, a 3D variational autoencoder (VAE) architecture and evaluate cAE and VAE for this task. Concretely, we pose the paranasal anomaly detection as an unsupervised anomaly detection problem. Thereby, we are able to reduce the labelling effort of the clinicians as we only use healthy samples during training. Additionally, we can classify any type of anomaly that differs from the training distribution. We train our 3D cAE and VAE to learn a latent representation of healthy maxillary sinus volumes using L1 reconstruction loss. During inference, we use the reconstruction error to classify between normal and anomalous maxillary sinuses. We extract sub-volumes from larger head and neck MRIs and analyse the effect of different fields of view on the detection performance. Finally, we report which anomalies are easiest and hardest to classify using our approach. Our results demonstrate the feasibility of unsupervised detection of paranasal anomalies from MRIs with an AUPRC of 85% and 80% for cAE and VAE, respectively

Multiple Instance Ensembling For Paranasal Anomaly Classification In The Maxillary Sinus

Paranasal anomalies are commonly discovered during routine radiological screenings and can present with a wide range of morphological features. This diversity can make it difficult for convolutional neural networks (CNNs) to accurately classify these anomalies, especially when working with limited datasets. Additionally, current approaches to paranasal anomaly classification are constrained to identifying a single anomaly at a time. These challenges necessitate the need for further research and development in this area. In this study, we investigate the feasibility of using a 3D convolutional neural network (CNN) to classify healthy maxillary sinuses (MS) and MS with polyps or cysts. The task of accurately identifying the relevant MS volume within larger head and neck Magnetic Resonance Imaging (MRI) scans can be difficult, but we develop a straightforward strategy to tackle this challenge. Our end-to-end solution includes the use of a novel sampling technique that not only effectively localizes the relevant MS volume, but also increases the size of the training dataset and improves classification results. Additionally, we employ a multiple instance ensemble prediction method to further boost classification performance. Finally, we identify the optimal size of MS volumes to achieve the highest possible classification performance on our dataset. With our multiple instance ensemble prediction strategy and sampling strategy, our 3D CNNs achieve an F1 of 0.85 whereas without it, they achieve an F1 of 0.70. We demonstrate the feasibility of classifying anomalies in the MS. We propose a data enlarging strategy alongside a novel ensembling strategy that proves to be beneficial for paranasal anomaly classification in the MS

An objective validation of polyp and instrument segmentation methods in colonoscopy through Medico 2020 polyp segmentation and MedAI 2021 transparency challenges

Automatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps. However, detecting polyps during the live examination can be challenging due to various factors such as variation of skills and experience among the endoscopists, lack of attentiveness, and fatigue leading to a high polyp miss-rate. Deep learning has emerged as a promising solution to this challenge as it can assist endoscopists in detecting and classifying overlooked polyps and abnormalities in real time. In addition to the algorithm's accuracy, transparency and interpretability are crucial to explaining the whys and hows of the algorithm's prediction. Further, most algorithms are developed in private data, closed source, or proprietary software, and methods lack reproducibility. Therefore, to promote the development of efficient and transparent methods, we have organized the "Medico automatic polyp segmentation (Medico 2020)" and "MedAI: Transparency in Medical Image Segmentation (MedAI 2021)" competitions. We present a comprehensive summary and analyze each contribution, highlight the strength of the best-performing methods, and discuss the possibility of clinical translations of such methods into the clinic. For the transparency task, a multi-disciplinary team, including expert gastroenterologists, accessed each submission and evaluated the team based on open-source practices, failure case analysis, ablation studies, usability and understandability of evaluations to gain a deeper understanding of the models' credibility for clinical deployment. Through the comprehensive analysis of the challenge, we not only highlight the advancements in polyp and surgical instrument segmentation but also encourage qualitative evaluation for building more transparent and understandable AI-based colonoscopy systems

User Authentication in Cloud Computing- Using Seed Chain Based One Time Password (OTP)

Abstract-Cloud computing has emerged as a popular model in computing world to support processing large volumetric data using clusters of commodity computers. It is the latest effort in delivering computing resources as a service. It is used to describe both a platform and a type of application, therefore removing the need of providing these services themselves. This can for example lead to cost savings, better resource utilization and removing the need of technical expertise for the customers. However, cloud services also present a couple of issues. Since the resources are put under another provider, the customer will have no control over the situation. Since the control of services and data needed for the everyday-run of a corporation is being handled by another company, further issues needs to be concerned. The consumer needs to trust the provider, and know that they handle their data in a correct manner, and that resources can be accessed when needed. This thesis focuses on authentication in cloud services. The current solutions used today to login to cloud services have been investigated and concluded that they don't satisfy the needs for cloud services. They are either insecure or complex. This thesis have resulted in an authentication and registration method that is both secure and easy to use, therefore fulfilling the needs of cloud service authentication. The conclusions that can be drawn is that the proposed security solution in this thesis work functions very well, and provide good security together with an ease of use for the clients who don't have so much technical knowledge

A numerical study on the effect of initial void ratio and areal extent of heterogeneity on instability onset in granular media

by Debayan Bhattacharya and Amit Prashan

Mesh size sensitivity and effect of perturbation intensity on coupled undrained instability analysis in sands

by Debayan Bhattacharya and Amit Prashan

A study of the impact of psychiatric distress on coping responses and the levels of anxiety, depression, and suicidal ideation among undergraduate nursing students

Background: Entering medical professional courses is stressful to students in general and more so in a profession like nursing. Identifying the most effective methods of coping strategies with psychiatric distress may help to reduce mental health issues. This study was conducted to estimate the levels of psychiatric distress, anxiety, depression, and suicidal ideation, and their correlation with coping responses among undergraduate nursing students. Methodology and Participants: Sixty-eight 1st-year BSC nursing students participated in the study. The students were surveyed online using Google Forms. Participants had to fill up five scales: General Health Questionnaire (GHQ-28); Coping response inventory; State-Trait Anxiety Inventory (STAI); Beck Depression Inventory (BDI); and Adult Suicidal Ideation Questionnaire (ASIQ). Results: Thirty-nine students (57.35%) were found to be facing psychiatric distress (GHQ-28 Score >4). Sixteen (23.5%) had moderate depression and 13 (19.1%) had severe depression scores. Psychiatric distress scores were positively correlated with Depression, Anxiety, and Suicidal Ideation scores. A positive correlation was also found between suicidal ideation and anxiety and depression. A significant positive correlation was found between emotional discharge and acceptance/resignation (coping strategies) and GHQ, STAI, BDI, and ASIQ Scores. Positive reappraisal (PR), seeking guidance and support (SG), and problem-solving were the three coping strategies that had negative correlations with psychiatric distress, state anxiety, and suicidal ideation. Conclusion: Psychiatric distress increased depression levels, anxiety as well as suicidal ideation. Emotional discharge was identified as the most commonly used coping response. PR, SG, and problem-solving were the three most effective coping strategies which helped in reducing perceived stress levels