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Titlebook: Marine Protists; Diversity and Dynami Susumu Ohtsuka,Toshinobu Suzaki,Fabrice Not Book 2015 Springer Japan 2015 Aquatic ecosystem.Chemosynt

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51#
發(fā)表于 2025-3-30 09:13:36 | 只看該作者
Stuart D. Syml annotation is time-consuming and requires specialized expertise. Semi-supervised segmentation methods that leverage both labeled and unlabeled data have shown promise, with contrastive learning emerging as a particularly effective approach. In this paper, we propose a contrastive learning strategy
52#
發(fā)表于 2025-3-30 13:12:07 | 只看該作者
53#
發(fā)表于 2025-3-30 19:54:50 | 只看該作者
Katsunori Kimotols could be leveraged by utilizing either transfer learning or semi-supervised learning on a limited number of strong labels from manual annotation. However, over-fitting could potentially arise due to the small data size. This work develops a dual-branch network to improve segmentation on OOD data
54#
發(fā)表于 2025-3-31 00:23:29 | 只看該作者
Noritoshi Suzuki,Fabrice Notre repetitive and cumbersome, only the largest lesion is identified leaving others of potential importance unmentioned. Automated deep learning-based methods for lesion detection have been proposed in literature to help relieve their tasks with the publicly available DeepLesion dataset (32,735 lesio
55#
發(fā)表于 2025-3-31 04:01:26 | 只看該作者
Yasuhide Nakamura,Noritoshi Suzuki availability of well-labeled data. In practice, it is a great challenge to obtain a large high-quality labeled dataset, especially for the medical image segmentation task, which generally needs pixel-wise labels, and the inaccurate label (noisy label) may significantly degrade the segmentation perf
56#
發(fā)表于 2025-3-31 05:03:55 | 只看該作者
57#
發(fā)表于 2025-3-31 13:12:16 | 只看該作者
Takashi Kamiyamare repetitive and cumbersome, only the largest lesion is identified leaving others of potential importance unmentioned. Automated deep learning-based methods for lesion detection have been proposed in literature to help relieve their tasks with the publicly available DeepLesion dataset (32,735 lesio
58#
發(fā)表于 2025-3-31 16:32:19 | 只看該作者
59#
發(fā)表于 2025-3-31 18:43:06 | 只看該作者
60#
發(fā)表于 2025-3-31 21:54:58 | 只看該作者
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