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Titlebook: Diabetic Foot Ulcers Grand Challenge; Third Challenge, DFU Moi Hoon Yap,Connah Kendrick,Bill Cassidy Conference proceedings 2023 The Editor

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樓主: Impacted
11#
發(fā)表于 2025-3-23 12:45:26 | 只看該作者
https://doi.org/10.1007/978-3-663-05717-8 cross validation and Test Time Augmentation. In the validation phase of DFUC2022, HarDNet-DFUS achieved 0.7063 mean Dice and was ranked third among all participants. In the final testing phase of DFUC2022, it achieved 0.7287 mean Dice and was the first place winner. The code is available on ..
12#
發(fā)表于 2025-3-23 17:48:38 | 只看該作者
13#
發(fā)表于 2025-3-23 18:34:11 | 只看該作者
Jürg Kuster,Christian Bachmann,Roger Wüstcessing step. The obtained results on the DFUC2022 challenge dataset show that our improvements can boost overall performance for ulcer segmentation tasks, even in scenarios where targeted structures are heterogeneous and under high imbalance conditions in the evaluated dataset. With our approach we achieved 9th place with a Dice score of 0.6975.
14#
發(fā)表于 2025-3-24 02:16:54 | 只看該作者
HarDNet-DFUS: Enhancing Backbone and?Decoder of?HarDNet-MSEG for?Diabetic Foot Ulcer Image Segmentat cross validation and Test Time Augmentation. In the validation phase of DFUC2022, HarDNet-DFUS achieved 0.7063 mean Dice and was ranked third among all participants. In the final testing phase of DFUC2022, it achieved 0.7287 mean Dice and was the first place winner. The code is available on ..
15#
發(fā)表于 2025-3-24 04:59:23 | 只看該作者
16#
發(fā)表于 2025-3-24 09:50:02 | 只看該作者
Refined Mixup Augmentation for?Diabetic Foot Ulcer Segmentationcessing step. The obtained results on the DFUC2022 challenge dataset show that our improvements can boost overall performance for ulcer segmentation tasks, even in scenarios where targeted structures are heterogeneous and under high imbalance conditions in the evaluated dataset. With our approach we achieved 9th place with a Dice score of 0.6975.
17#
發(fā)表于 2025-3-24 13:57:12 | 只看該作者
https://doi.org/10.1007/978-3-663-05717-8 deep learning classification networks. The presence of binary-identical duplicate images in datasets used to train deep learning algorithms is a well known issue that can introduce unwanted bias which can degrade network performance. However, the effect of visually similar non-identical images is a
18#
發(fā)表于 2025-3-24 17:39:42 | 只看該作者
19#
發(fā)表于 2025-3-24 21:28:48 | 只看該作者
20#
發(fā)表于 2025-3-25 01:54:28 | 只看該作者
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