synovitis
发表于 2025-3-23 11:12:48
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incision
发表于 2025-3-23 14:41:55
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.
可憎
发表于 2025-3-23 18:33:03
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Graphite
发表于 2025-3-23 22:23:18
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欢乐东方
发表于 2025-3-24 05:26:16
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
Corroborate
发表于 2025-3-24 08:27:50
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
LAITY
发表于 2025-3-24 12:37:08
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Restenosis
发表于 2025-3-24 17:25:00
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Gorilla
发表于 2025-3-24 19:02:25
: 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
出价
发表于 2025-3-25 00:26:50
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.