A5027843290- Cutaneous Melanoma Detection and Management
- AI in cancer detection
- Imbalanced Data Classification Techniques
- Social Media in Health Education
- Artificial Intelligence in Healthcare and Education
- Anomaly Detection Techniques and Applications
- Hemodynamic Monitoring and Therapy
- Vaccine Coverage and Hesitancy
- Digital Marketing and Social Media
- Body Image and Dysmorphia Studies
- COVID-19 Pandemic Impacts
- Radiomics and Machine Learning in Medical Imaging
- Image and Signal Denoising Methods
- Anesthesia and Pain Management
- Nausea and vomiting management
Rawalpindi Medical University
2017
The large language models GPT-4 Vision and Large Language Assistant are capable of understanding accurately differentiating between benign lesions melanoma, indicating potential incorporation into dermatologic care, medical research, education.
<sec> <title>UNSTRUCTURED</title> The large language models GPT-4 Vision and Large Language Assistant are capable of understanding accurately differentiating between benign lesions melanoma, indicating potential incorporation into dermatologic care, medical research, education. </sec>
Despite continued advancement in recent years, deep neural networks still rely on large amounts of training data to avoid overfitting. However, labeled for real-world applications such as healthcare is limited and difficult access given longstanding privacy, strict sharing policies. By manipulating image datasets the pixel or feature space, existing augmentation techniques represent one effective ways improve quantity diversity data. Here, we look advance by building upon emerging success...
We present a visual symptom checker that combines pre-trained Convolutional Neural Network (CNN) with Reinforcement Learning (RL) agent as Question Answering (QA) model. This method increases the classification confidence and accuracy of checker, decreases average number questions asked to narrow down differential diagnosis. A Deep Q-Network (DQN)-based RL learns how ask patient about presence symptoms in order maximize probability correctly identifying underlying condition. The uses...
The evolution of behavior dermatology patients has seen significantly accelerated change over the past decade, driven by surging availability and adoption digital tools platforms. Through our longitudinal analysis this within Tunisia a 35-year time frame, we identify behavioral patterns across economic cultural dimensions how have impacted those in preceding years. Throughout work, highlight witnessed effects available as experienced patients, conclude presenting vision for future can help...