Zobrazeno 1 - 10
of 653
pro vyhledávání: '"Domain-shift"'
Publikováno v:
IEEE Access, Vol 12, Pp 169879-169895 (2024)
Bearing fault diagnosis is a well-developed field and an active area of research in which the combination of model-free machine learning techniques with vibration data has become a popular approach. However, vibration data from rotating machines has
Externí odkaz:
https://doaj.org/article/ab9a922057b446aeb34f3e1d0e83d2b8
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 16799-16814 (2024)
Remote sensing (RS) visual question answering (VQA) is a task that answers questions about a given RS image by utilizing both image and textual information. However, existing methods in RS VQA overlook the fact that the ground truths in RS VQA benchm
Externí odkaz:
https://doaj.org/article/dda33c258c314686b35451ab6f8bcbc8
Autor:
Panpan Fu, Umi Kalsom Yusof
Publikováno v:
IEEE Access, Vol 12, Pp 129528-129540 (2024)
In practical applications, the issue of data imbalance inevitably rises. In most studies, the predominant focus regarding long-tailed class imbalance pertains to a setting within a single domain in which the training and test samples are presumed to
Externí odkaz:
https://doaj.org/article/d86647270a71491591eaa8cb434d3b1e
Publikováno v:
Informatics in Medicine Unlocked, Vol 48, Iss , Pp 101520- (2024)
Prototypical networks (PN) have emerged as one of multiple effective approaches for few-shot learning (FSL), even in medical image classification. This study focuses on implementing a PN for skin lesion classification to assess its performance, gener
Externí odkaz:
https://doaj.org/article/1ef067700d5f47dfb20a03d15bd99fa7
Publikováno v:
Informatics in Medicine Unlocked, Vol 44, Iss , Pp 101430- (2024)
The potential of deep neural networks in skin lesion classification has already been demonstrated to be on-par if not superior to the dermatologists’ diagnosis in experimental settings. However, the performance of these models usually deteriorates
Externí odkaz:
https://doaj.org/article/8430f2700fe64ed688b525b9f20e5cae
Evaluating Cellularity Estimation Methods: Comparing AI Counting with Pathologists’ Visual Estimates
Autor:
Tomoharu Kiyuna, Eric Cosatto, Kanako C. Hatanaka, Tomoyuki Yokose, Koji Tsuta, Noriko Motoi, Keishi Makita, Ai Shimizu, Toshiya Shinohara, Akira Suzuki, Emi Takakuwa, Yasunari Takakuwa, Takahiro Tsuji, Mitsuhiro Tsujiwaki, Mitsuru Yanai, Sayaka Yuzawa, Maki Ogura, Yutaka Hatanaka
Publikováno v:
Diagnostics, Vol 14, Iss 11, p 1115 (2024)
The development of next-generation sequencing (NGS) has enabled the discovery of cancer-specific driver gene alternations, making precision medicine possible. However, accurate genetic testing requires a sufficient amount of tumor cells in the specim
Externí odkaz:
https://doaj.org/article/d83d2d165ed54f929cd0611be8d9b972
Publikováno v:
Remote Sensing, Vol 16, Iss 8, p 1449 (2024)
High-resolution land cover mapping is crucial in various disciplines but is often hindered by the lack of accurately matched labels. Our study introduces an innovative deep learning methodology for effective land cover mapping, independent of matched
Externí odkaz:
https://doaj.org/article/cd7f235275a8461492ae7fec4d7afbe9
Autor:
Afnan M. Alhassan
Publikováno v:
IEEE Access, Vol 11, Pp 136350-136360 (2023)
The second most frequent disease in terms of diagnoses is breast cancer, which has had tremendous impact on women’s lives all around the world. The most frequent cause is a tumour formed as a result of abnormal cell divisions of tissues in the brea
Externí odkaz:
https://doaj.org/article/5349a6f559a74348b8f58edf2e714112
Publikováno v:
IEEE Access, Vol 11, Pp 82665-82673 (2023)
Cross-domain few-shot classification (CD-FSC) aims to develop few-shot classification models trained on seen domains but tested on unseen domains. However, the cross-domain setup poses a challenge in the form of domain shift between the training and
Externí odkaz:
https://doaj.org/article/ac25d389a03943fdad5ea96385f59365
Publikováno v:
IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 31, Pp 2570-2580 (2023)
Although deep learning (DL) techniques have been extensively researched in upper-limb myoelectric control, system robustness in cross-day applications is still very limited. This is largely caused by non-stable and time-varying properties of surface
Externí odkaz:
https://doaj.org/article/24bc2e66df374c97bf31e9c82b12fe78