Hemiparesis
发表于 2025-3-25 04:42:49
http://reply.papertrans.cn/63/6292/629129/629129_21.png
notion
发表于 2025-3-25 09:51:12
http://reply.papertrans.cn/63/6292/629129/629129_22.png
Additive
发表于 2025-3-25 14:50:36
http://reply.papertrans.cn/63/6292/629129/629129_23.png
laceration
发表于 2025-3-25 16:57:19
http://reply.papertrans.cn/63/6292/629129/629129_24.png
有恶意
发表于 2025-3-25 20:57:32
http://reply.papertrans.cn/63/6292/629129/629129_25.png
细微差别
发表于 2025-3-26 03:04:31
Iterative Training of Discriminative Models for the Generalized Hough Transformd the most confusable locations. In an iterative procedure, the training set is gradually enhanced by images from the development set on which the localization failed. The proposed technique is shown to substantially improve the object localization capabilities on long-leg radiographs.
弄脏
发表于 2025-3-26 08:00:36
Surgical Phases Detection from Microscope Videos by Combining SVM and HMMwhere six phases were identified by neurosurgeons. Cross-validation studies permitted to find a percentage of detected phases of 93% that will allow the use of the system in clinical applications such as post-operative videos indexation.
nerve-sparing
发表于 2025-3-26 12:24:31
http://reply.papertrans.cn/63/6292/629129/629129_28.png
解决
发表于 2025-3-26 13:27:04
Multiple Classifier Systems in Texton-Based Approach for the Classification of CT Images of Lungssifier systems helps to improve the results compared to single SVMs, combining over different texton sizes is not beneficial. The performance of the proposed system, with an accuracy of 95%, is similar to a recently proposed approach based on local binary patterns, which performs almost the best among other approaches in the literature.
RAGE
发表于 2025-3-26 20:52:07
Semisupervised Probabilistic Clustering of Brain MR Images Including Prior Clinical Informationmethods is performed. We show that the use of a limited amount of prior knowledge about cluster memberships can contribute to a better clustering performance in certain applications, while on the other hand the semisupervised clustering is quite robust to incorrect prior clustering knowledge.