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Titlebook: Medical Image Understanding and Analysis; 27th Annual Conferen Gordon Waiter,Tryphon Lambrou,Sharon Gordon Conference proceedings 2024 The

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樓主: 螺絲刀
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發(fā)表于 2025-3-23 11:12:48 | 只看該作者
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發(fā)表于 2025-3-23 14:41:55 | 只看該作者
A Deep Learning Based Approach to?Semantic Segmentation of?Lung Tumour Areas in?Gross Pathology Imags which produced a tumour pixel-wise accuracy of 69.7% (96.8% global accuracy) and tumour area IoU score of 0.616. This work on this novel application highlights the challenges with implementing a semantic segmentation model in this domain that have not been previously documented.
13#
發(fā)表于 2025-3-23 18:33:03 | 只看該作者
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發(fā)表于 2025-3-23 22:23:18 | 只看該作者
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發(fā)表于 2025-3-24 05:26:16 | 只看該作者
Efficient Semantic Segmentation of Nuclei in Histopathology Images Using Segformeris used as it combines the advantages of transformers and convolutional neural networks. To evaluate the performance of the models, dice evaluation metric is used. The proposed method achieved state-of-the-art results on the PanNuke dataset, with Segformer-b4 achieving a mean dice score of 0.845, an
16#
發(fā)表于 2025-3-24 08:27:50 | 只看該作者
Cross-Modality Deep Transfer Learning: Application to?Liver Segmentation in?CT and?MRIunt of training data, which is not available for MR. There are many CT datasets available compared to few MR datasets. The use of transfer learning can help to mitigate the problem of having a small amount of training data. We suggest training a U-Net deep learning model on the large publicly availa
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發(fā)表于 2025-3-24 12:37:08 | 只看該作者
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發(fā)表于 2025-3-24 17:25:00 | 只看該作者
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發(fā)表于 2025-3-24 19:02:25 | 只看該作者
: Cross-Domain Cell Detection in?Histopathology Images via?Contextual Regularizations a reconstruction task that involves masking the high-level semantic features either stochastically or adaptively. Then, a transformer-based reconstruction head is designed to recover the original features based on partial observations. Additionally, CR can be seamlessly integrated with various dee
20#
發(fā)表于 2025-3-25 00:26:50 | 只看該作者
A New Similarity Metric for?Deformable Registration of?MALDI–MS and?MRI Imageslarity metric for deformable registration, based on the update of distance transformation values. We show that our method limits the intensity distortions while providing precisely registered images, on both synthetic and mouse brain images.
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