A5030632857- AI in cancer detection
- Digital Imaging for Blood Diseases
- Monoclonal and Polyclonal Antibodies Research
- Anomaly Detection Techniques and Applications
- Data-Driven Disease Surveillance
- Geographic Information Systems Studies
- Glycosylation and Glycoproteins Research
- Biomedical Text Mining and Ontologies
- Advanced Clustering Algorithms Research
- vaccines and immunoinformatics approaches
- Digital Media Forensic Detection
- Medical Image Segmentation Techniques
The University of Texas at Arlington
2024-2025
New Jersey Institute of Technology
2021
Pathology plays a critical role in diagnosing wide range of diseases, yet existing approaches often rely heavily on task-specific models trained extensive, well-labeled datasets. These methods face sustainability challenges due to the diversity pathologies and labor-intensive nature data collection. To address these limitations, we highlight need for universal multimodal embeddings that can support multiple downstream tasks. Previous involve fine-tuning CLIP-based models, which handle images...
Whole slide images (WSIs) play a vital role in cancer diagnosis and prognosis. However, their gigapixel resolution, lack of pixel-level annotations, reliance on unimodal visual data present challenges for accurate efficient computational analysis. Existing methods typically divide WSIs into thousands patches, which increases demands makes it challenging to effectively focus diagnostically relevant regions. Furthermore, these frequently rely feature extractors pretrained natural images, are...
Thousands of results scientific publications on the topic COVID-19 have emerged within a short time, raising significant challenges for scholars to organize research, or even synthesize knowledge in timely comprehensive manner. To facilitate this, we developed geospatial and semantic mapping platform search these large unmapped digital collections. The map visualizes research topics based customized natural language processing algorithms, which helps users identify their content specific...