NAG
发表于 2025-3-26 21:35:26
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analogous
发表于 2025-3-27 03:41:48
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露天历史剧
发表于 2025-3-27 08:14:50
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儿童
发表于 2025-3-27 09:48:36
Automatic Computer-Aided Histopathologic Segmentation for Nasopharyngeal Carcinoma Using Transformeon. To validate and compare the transformer framework with various CNN-based methods, experiments have been conducted on the clinical dataset collection of NPC. The transformer framework outperformed the state-of-the-art pure CNN-based methods in AUC and recall. Especially, our framework achieved 2.
Crumple
发表于 2025-3-27 14:06:36
Accurate Breast Tumor Identification Using Computational Ultrasound Image Features, proposed algorithm achieved a diagnostic accuracy of 89.32% and a significant area under curve (AUC) of 0.9473 with the repeated cross-validation scheme. In conclusion, our algorithm shows superior performance over the existing classical methods and can be potentially applied to breast cancer scree
冒烟
发表于 2025-3-27 20:04:25
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防止
发表于 2025-3-27 22:07:04
,Is More Always Better? Effects of Patch Sampling in Distinguishing Chronic Lymphocytic Leukemia froransformation; RT) has important clinical implications that greatly influence patient management. However, distinguishing between these disease phases on histologic grounds may be challenging in routine practice due to the presence of similar structures and homogeneous intensity, among others. In th
Flirtatious
发表于 2025-3-28 03:08:22
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colony
发表于 2025-3-28 06:59:01
,MLCN: Metric Learning Constrained Network for Whole Slide Image Classification with Bilinear Gated eved good results, the classification performance is still unsatisfactory because the learned features of WSI lack discrimination and the correlation among sub-characteristics of tumor images are ignored. In this paper, we proposed a Metric Learning Constraint Network (referred to as MLCN). Particul
resuscitation
发表于 2025-3-28 13:47:25
,NucDETR: End-to-End Transformer for Nucleus Detection in Histopathology Images,pensive task if done manually by experienced clinicians, and is also prone to subjectivity and inconsistency. Alternatively, the advancement in computer vision-based analysis enables the automatic detection of cancerous nuclei; however, the task poses several challenges due to the heterogeneity in t