augmented-multimodal-fusion-for-optimized-brain-tumor-detection

Augmented Multimodal Fusion for Optimized Brain Tumor Detection: Evaluation and Comparative Analysis

Brain tumors represent a significant medical challenge, necessitating accurate and efficient detection methods for timely intervention. This work ...

Thapar Institute of Engineering and Technology

Artificial intelligence (AI) has several uses in the healthcare industry, some of which include healthcare management, medical forecasting, practical making of decisions, and diagnosis. AI technologies have reached human-like performance, but their use is limited since they are still largely viewed as opaque black boxes. This distrust remains the primary factor for their limited real application, particularly in healthcare. As a result, there is a need for interpretable predictors that provide better predictions and also explain their predictions.

DeepXplainer: An interpretable deep learning based approach for lung cancer detection using explainable artificial intelligence.

The rapid expansion of digital communication has driven online social networks (OSNs) to surpass conventional news outlets, seamlessly integrating into our daily lives. This dependence on social network data has led to the emergence of various research paths, including event detection. However, such research tasks require large scale labelled social network data, and annotating such vast amounts of data is nearly impractical and often considered unfeasible. In addition, established literature encounters significant hurdles when it comes to detect unseen or new events, even after acquiring a substantial amount of training data. This marks a new era, emphasizing the importance of leveraging minimal data and embracing broader generalizations, much like human understanding of information. To this end, we proposed AttendFew, a model to detect events in X (formely X) with limited data (Few-shot learning) which detects events while mitigating data dependency, even for unseen events, aligning with the current need for less data and greater adaptability. The proposed method encodes the posts (also known as "posts") with BERTweet and Graph Attention Networks to capture both contextual and structural aspects of social network data and further, stacked attention will be applied to make the model attend local and global context effectively and enhances the ability to understand complex data of social network. Furthermore, the amalgamation of the feature score and Multi-layer Perceptron (MLP) facilitates class matching, aligning the derived features with their associated classifications. This operation emphasizes pivotal dimensions within the feature space while addressing data sparsity. AttendFew is evaluated on real-world datasets and exhibits significantly better performance than state-of-the-art (SOTA) and other baseline methods in terms of accuracy, F1-score. This study represents an attempt to utilize a few-shot learning model in addressing the challenges posed by the sparsity and dynamism of online social networks for event detection.

Article	Year
DeepXplainer: An interpretable deep learning based approach for lung cancer detection using explainable artificial intelligence. Computer methods and programs in biomedicine\| PubMed ID: 37897989	2024
An attention-enhanced few-shot model for event detection in online social networks. Scientific reports\| PubMed ID: 40295600	2025

Niyaz Ahmad Wani

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