Výsledky vyhledávání - "Rachmadi, Muhammad Febrian"

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Few-shot medical image classification with simple shape and texture text descriptors using vision-language models

Autor: Byra, Michal, Rachmadi, Muhammad Febrian, Skibbe, Henrik

In this work, we investigate the usefulness of vision-language models (VLMs) and large language models for binary few-shot classification of medical images. We utilize the GPT-4 model to generate text descriptors that encapsulate the shape and textur

Externí odkaz: http://arxiv.org/abs/2308.04005

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White Matter Hyperintensities Segmentation Using Probabilistic TransUNet

Autor: Eldianto, Muhammad Noor Dwi, Rachmadi, Muhammad Febrian, Jatmiko, Wisnu

White Matter Hyperintensities (WMH) are areas of the brain that have higher intensity than other normal brain regions on Magnetic Resonance Imaging (MRI) scans. WMH is often associated with small vessel disease in the brain, making early detection of

Externí odkaz: http://arxiv.org/abs/2305.03912

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Improving Segmentation of Objects with Varying Sizes in Biomedical Images using Instance-wise and Center-of-Instance Segmentation Loss Function

Autor: Rachmadi, Muhammad Febrian, Poon, Charissa, Skibbe, Henrik

In this paper, we propose a novel two-component loss for biomedical image segmentation tasks called the Instance-wise and Center-of-Instance (ICI) loss, a loss function that addresses the instance imbalance problem commonly encountered when using pix

Externí odkaz: http://arxiv.org/abs/2304.06229

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Hierarchical Vision Transformers for Cardiac Ejection Fraction Estimation

Autor: Fazry, Lhuqita, Haryono, Asep, Nissa, Nuzulul Khairu, Sunarno, Hirzi, Naufal Muhammad, Rachmadi, Muhammad Febrian, Jatmiko, Wisnu

Publikováno v: IWBIS 2022 International Workshop on Big Data and Information Security (IWBIS) 2022

The left ventricular of ejection fraction is one of the most important metric of cardiac function. It is used by cardiologist to identify patients who are eligible for lifeprolonging therapies. However, the assessment of ejection fraction suffers fro

Externí odkaz: http://arxiv.org/abs/2304.00177

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An automated pipeline to create an atlas of in situ hybridization gene expression data in the adult marmoset brain

Autor: Poon, Charissa, Rachmadi, Muhammad Febrian, Byra, Michal, Schlachter, Matthias, Xu, Binbin, Shimogori, Tomomi, Skibbe, Henrik

We present the first automated pipeline to create an atlas of in situ hybridization gene expression in the adult marmoset brain in the same stereotaxic space. The pipeline consists of segmentation of gene expression from microscopy images and registr

Externí odkaz: http://arxiv.org/abs/2303.06857

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