11 hasil untuk "Amirreza Mahbod"
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Artikel Jurnal Teks lengkap
Special Issue on “Advances in Skin Lesion Image Analysis Using Machine Learning Approaches”
Skin diseases are widespread and a frequent occurrence in general practice [...]
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Artikel Jurnal Teks lengkap
Special Issue on “Machine Learning-Aided Medical Image Analysis”
With the advancement of computers and digitalization, many medical records and data, including medical images, can now be stored and analyzed using semi- or fully automated computerized approaches [...]
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Artikel Jurnal
A dual decoder U-Net-based model for nuclei instance segmentation in hematoxylin and eosin-stained histological images
Even in the era of precision medicine, with various molecular tests based on omics technologies available to improve the diagnosis process, microscopic analysis of images derived from stained tissue sections remains crucial for diagnostic and treatment decisions. Among other cellular features, both…
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Artikel Jurnal Teks lengkap
Integrating Foundation Model Features into Graph Neural Network and Fusing Predictions with Standard Fine-Tuned Models for Histology Image Classification
Histopathological image classification using computational methods such as fine-tuned convolutional neural networks (CNNs) has gained significant attention in recent years. Graph neural networks (GNNs) have also emerged as strong alternatives, often employing CNNs or vision transformers (ViTs) as…
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Artikel Jurnal
Improving generalization capability of deep learning-based nuclei instance segmentation by non-deterministic train time and deterministic test time stain normalization
With the advent of digital pathology and microscopic systems that can scan and save whole slide histological images automatically, there is a growing trend to use computerized methods to analyze acquired images. Among different histopathological image analysis tasks, nuclei instance segmentation…
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Artikel Jurnal
A multi-stage auto-context deep learning framework for tissue and nuclei segmentation and classification in H&E-stained histological images of advanced melanoma
Melanoma is the most lethal form of skin cancer, with an increasing incidence rate worldwide. Analyzing histological images of melanoma by localizing and classifying tissues and cell nuclei is considered the gold standard method for diagnosis and treatment options for patients. While many…
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Artikel Jurnal Teks lengkap
NucFuseRank: Dataset Fusion and Performance Ranking for Nuclei Instance Segmentation
Nuclei instance segmentation in hematoxylin and eosin (H&E)-stained images plays an important role in automated histological image analysis, with various applications in downstream tasks. While several machine learning and deep learning approaches have been proposed for nuclei instance…
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Artikel Jurnal Teks lengkap
Investigating the Impact of the Bit Depth of Fluorescence-Stained Images on the Performance of Deep Learning-Based Nuclei Instance Segmentation
Nuclei instance segmentation can be considered as a key point in the computer-mediated analysis of histological fluorescence-stained (FS) images. Many computer-assisted approaches have been proposed for this task, and among them, supervised deep learning (DL) methods deliver the best performances…
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Artikel Jurnal Teks lengkap
FUSeg: The Foot Ulcer Segmentation Challenge
Wound care professionals provide proper diagnosis and treatment with heavy reliance on images and image documentation. Segmentation of wound boundaries in images is a key component of the care and diagnosis protocol since it is important to estimate the area of the wound and provide quantitative…
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Artikel Jurnal
NuInsSeg: A fully annotated dataset for nuclei instance segmentation in H&E-stained histological images
Abstract In computational pathology, automatic nuclei instance segmentation plays an essential role in whole slide image analysis. While many computerized approaches have been proposed for this task, supervised deep learning (DL) methods have shown superior segmentation performances compared to…