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- W4289236495 abstract "We propose iW-Net, a deep learning model that allows for both automatic and interactive segmentation of lung nodules in computed tomography images. iW-Net is composed of two blocks: the first one provides an automatic segmentation and the second one allows to correct it by analyzing 2 points introduced by the user in the nodule's boundary. For this purpose, a physics inspired weight map that takes the user input into account is proposed, which is used both as a feature map and in the system's loss function. Our approach is extensively evaluated on the public LIDC-IDRI dataset, where we achieve a state-of-the-art performance of 0.55 intersection over union vs the 0.59 inter-observer agreement. Also, we show that iW-Net allows to correct the segmentation of small nodules, essential for proper patient referral decision, as well as improve the segmentation of the challenging non-solid nodules and thus may be an important tool for increasing the early diagnosis of lung cancer." @default.
- W4289236495 created "2022-08-01" @default.
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- W4289236495 date "2018-11-30" @default.
- W4289236495 modified "2023-10-01" @default.
- W4289236495 title "iW-Net: an automatic and minimalistic interactive lung nodule segmentation deep network" @default.
- W4289236495 doi "https://doi.org/10.48550/arxiv.1811.12789" @default.
- W4289236495 hasPublicationYear "2018" @default.
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