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pro vyhledávání: '"Adiga V, Sukesh"'
Semi-supervised learning relaxes the need of large pixel-wise labeled datasets for image segmentation by leveraging unlabeled data. A prominent way to exploit unlabeled data is to regularize model predictions. Since the predictions of unlabeled data
Externí odkaz:
http://arxiv.org/abs/2310.16099
Autor:
Murugesan, Balamurali, Adiga V, Sukesh, Liu, Bingyuan, Lombaert, Hervé, Ayed, Ismail Ben, Dolz, Jose
Ensuring reliable confidence scores from deep networks is of pivotal importance in critical decision-making systems, notably in the medical domain. While recent literature on calibrating deep segmentation networks has led to significant progress, the
Externí odkaz:
http://arxiv.org/abs/2303.06268
Learning similarity is a key aspect in medical image analysis, particularly in recommendation systems or in uncovering the interpretation of anatomical data in images. Most existing methods learn such similarities in the embedding space over image se
Externí odkaz:
http://arxiv.org/abs/2206.09068
Semi-supervised segmentation tackles the scarcity of annotations by leveraging unlabeled data with a small amount of labeled data. A prominent way to utilize the unlabeled data is by consistency training which commonly uses a teacher-student network,
Externí odkaz:
http://arxiv.org/abs/2203.05682
Publikováno v:
In Medical Image Analysis January 2024 91
Segmentation using deep learning has shown promising directions in medical imaging as it aids in the analysis and diagnosis of diseases. Nevertheless, a main drawback of deep models is that they require a large amount of pixel-level labels, which are
Externí odkaz:
http://arxiv.org/abs/2004.03046
Autor:
Adiga V, Sukesh, Sivaswamy, Jayanthi
Fingerprint is a common biometric used for authentication and verification of an individual. These images are degraded when fingers are wet, dirty, dry or wounded and due to the failure of the sensors, etc. The extraction of the fingerprint from a de
Externí odkaz:
http://arxiv.org/abs/1812.10191
Publikováno v:
IEEE journal of biomedical and health informatics [IEEE J Biomed Health Inform] 2022 Sep; Vol. 26 (9), pp. 4599-4610. Date of Electronic Publication: 2022 Sep 09.