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Medical Image Analysis
Scopus
ISSN 1361-8415 · Elsevier BV · 38 artikel di indeks
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Artikel Jurnal
Medical SAM adapter: Adapting segment anything model for medical image segmentation
The Segment Anything Model (SAM) has recently gained popularity in the field of image segmentation due to its impressive capabilities in various segmentation tasks and its prompt-based interface. However, recent studies and individual experiments have shown that SAM underperforms in medical image…
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Artikel Jurnal
CellViT: Vision Transformers for precise cell segmentation and classification
Nuclei detection and segmentation in hematoxylin and eosin-stained (H&E) tissue images are important clinical tasks and crucial for a wide range of applications. However, it is a challenging task due to nuclei variances in staining and size, overlapping boundaries, and nuclei clustering. While…
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Artikel Jurnal
TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers
Medical image segmentation is crucial for healthcare, yet convolution-based methods like U-Net face limitations in modeling long-range dependencies. To address this, Transformers designed for sequence-to-sequence predictions have been integrated into medical image segmentation. However, a…
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Artikel Jurnal Teks lengkap
Segment anything model for medical image analysis: An experimental study
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Artikel Jurnal Teks lengkap
SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining
Despite advances in data augmentation and transfer learning, convolutional neural networks (CNNs) difficultly generalise to unseen domains. When segmenting brain scans, CNNs are highly sensitive to changes in resolution and contrast: even within the same MRI modality, performance can decrease…
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Artikel Jurnal Teks lengkap
Deep learning based synthesis of MRI, CT and PET: Review and analysis
Medical image synthesis represents a critical area of research in clinical decision-making, aiming to overcome the challenges associated with acquiring multiple image modalities for an accurate clinical workflow. This approach proves beneficial in estimating an image of a desired modality from a…
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Explainable artificial intelligence (XAI) in deep learning-based medical image analysis
With an increase in deep learning-based methods, the call for explainability of such methods grows, especially in high-stakes decision making areas such as medical image analysis. This survey presents an overview of explainable artificial intelligence (XAI) used in deep learning-based medical image…
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The Liver Tumor Segmentation Benchmark (LiTS)
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention…
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Recent advances and clinical applications of deep learning in medical image analysis
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Boundary-aware context neural network for medical image segmentation
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Artikel Jurnal Teks lengkap
VerSe: A Vertebrae labelling and segmentation benchmark for multi-detector CT images
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Artikel Jurnal Teks lengkap
Analysis of the ISIC image datasets: Usage, benchmarks and recommendations
The International Skin Imaging Collaboration (ISIC) datasets have become a leading repository for researchers in machine learning for medical image analysis, especially in the field of skin cancer detection and malignancy assessment. They contain tens of thousands of dermoscopic photographs…
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A survey on incorporating domain knowledge into deep learning for medical image analysis
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Artikel Jurnal Teks lengkap
Federated learning for computational pathology on gigapixel whole slide images
Deep Learning-based computational pathology algorithms have demonstrated profound ability to excel in a wide array of tasks that range from characterization of well known morphological phenotypes to predicting non human-identifiable features from histology such as molecular alterations. However…
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Artikel Jurnal Teks lengkap
Autoencoders for unsupervised anomaly segmentation in brain MR images: A comparative study