Výsledky vyhledávání - "Popovic Milos R."

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UlcerGPT: A Multimodal Approach Leveraging Large Language and Vision Models for Diabetic Foot Ulcer Image Transcription

Autor: Basiri, Reza, Abedi, Ali, Nguyen, Chau, Popovic, Milos R., Khan, Shehroz S.

Diabetic foot ulcers (DFUs) are a leading cause of hospitalizations and lower limb amputations, placing a substantial burden on patients and healthcare systems. Early detection and accurate classification of DFUs are critical for preventing serious c

Externí odkaz: http://arxiv.org/abs/2410.01989

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Implicit Dynamical Flow Fusion (IDFF) for Generative Modeling

Autor: Rezaei, Mohammad R., Krishnan, Rahul G., Popovic, Milos R., Lankarany, Milad

Conditional Flow Matching (CFM) models can generate high-quality samples from a non-informative prior, but they can be slow, often needing hundreds of network evaluations (NFE). To address this, we propose Implicit Dynamical Flow Fusion (IDFF); IDFF

Externí odkaz: http://arxiv.org/abs/2409.14599

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Synthesizing Diabetic Foot Ulcer Images with Diffusion Model

Autor: Basiri, Reza, Manji, Karim, Harton, Francois, Poonja, Alisha, Popovic, Milos R., Khan, Shehroz S.

Diabetic Foot Ulcer (DFU) is a serious skin wound requiring specialized care. However, real DFU datasets are limited, hindering clinical training and research activities. In recent years, generative adversarial networks and diffusion models have emer

Externí odkaz: http://arxiv.org/abs/2310.20140

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Supervised and Unsupervised Deep Learning Approaches for EEG Seizure Prediction

Autor: Georgis-Yap, Zakary, Popovic, Milos R., Khan, Shehroz S.

Publikováno v: Journal of Health Informatics Research, 2024

Epilepsy affects more than 50 million people worldwide, making it one of the world's most prevalent neurological diseases. The main symptom of epilepsy is seizures, which occur abruptly and can cause serious injury or death. The ability to predict th

Externí odkaz: http://arxiv.org/abs/2304.14922

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Deep Direct Discriminative Decoders for High-dimensional Time-series Data Analysis

Autor: Rezaei, Mohammad R., Popovic, Milos R., Lankarany, Milad, Yousefi, Ali

The state-space models (SSMs) are widely utilized in the analysis of time-series data. SSMs rely on an explicit definition of the state and observation processes. Characterizing these processes is not always easy and becomes a modeling challenge when

Externí odkaz: http://arxiv.org/abs/2205.10947

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