Zobrazeno 1 - 10
of 727
pro vyhledávání: '"RASOOL, GHULAM"'
In this study, we aim to enhance radiology reporting by improving both the conciseness and structured organization of findings (also referred to as templating), specifically by organizing information according to anatomical regions. This structured a
Externí odkaz:
http://arxiv.org/abs/2411.05042
Autor:
Koutsoubis, Nikolas, Waqas, Asim, Yilmaz, Yasin, Ramachandran, Ravi P., Schabath, Matthew, Rasool, Ghulam
Artificial Intelligence (AI) has demonstrated significant potential in automating various medical imaging tasks, which could soon become routine in clinical practice for disease diagnosis, prognosis, treatment planning, and post-treatment surveillanc
Externí odkaz:
http://arxiv.org/abs/2409.16340
Machine learning (ML) and Artificial Intelligence (AI) have fueled remarkable advancements, particularly in healthcare. Within medical imaging, ML models hold the promise of improving disease diagnoses, treatment planning, and post-treatment monitori
Externí odkaz:
http://arxiv.org/abs/2406.12815
Autor:
Waqas, Asim, Tripathi, Aakash, Stewart, Paul, Naeini, Mia, Schabath, Matthew B., Rasool, Ghulam
Cancer clinics capture disease data at various scales, from genetic to organ level. Current bioinformatic methods struggle to handle the heterogeneous nature of this data, especially with missing modalities. We propose PARADIGM, a Graph Neural Networ
Externí odkaz:
http://arxiv.org/abs/2406.08521
Continual learning focuses on learning non-stationary data distribution without forgetting previous knowledge. Rehearsal-based approaches are commonly used to combat catastrophic forgetting. However, these approaches suffer from a problem called "reh
Externí odkaz:
http://arxiv.org/abs/2405.11829
Developing accurate machine learning models for oncology requires large-scale, high-quality multimodal datasets. However, creating such datasets remains challenging due to the complexity and heterogeneity of medical data. To address this challenge, w
Externí odkaz:
http://arxiv.org/abs/2405.07460
Autor:
Waqas, Asim, Tripathi, Aakash, Ahmed, Sabeen, Mukund, Ashwin, Farooq, Hamza, Schabath, Matthew B., Stewart, Paul, Naeini, Mia, Rasool, Ghulam
Multi-omics research has enhanced our understanding of cancer heterogeneity and progression. Investigating molecular data through multi-omics approaches is crucial for unraveling the complex biological mechanisms underlying cancer, thereby enabling m
Externí odkaz:
http://arxiv.org/abs/2405.08226
Artificial intelligence (AI) and neuroscience share a rich history, with advancements in neuroscience shaping the development of AI systems capable of human-like knowledge retention. Leveraging insights from neuroscience and existing research in adve
Externí odkaz:
http://arxiv.org/abs/2404.14588
Autor:
Hartsock, Iryna, Rasool, Ghulam
Medical vision-language models (VLMs) combine computer vision (CV) and natural language processing (NLP) to analyze visual and textual medical data. Our paper reviews recent advancements in developing VLMs specialized for healthcare, focusing on mode
Externí odkaz:
http://arxiv.org/abs/2403.02469
The advancements in data acquisition, storage, and processing techniques have resulted in the rapid growth of heterogeneous medical data. Integrating radiological scans, histopathology images, and molecular information with clinical data is essential
Externí odkaz:
http://arxiv.org/abs/2310.01438