Zobrazeno 1 - 10
of 355
pro vyhledávání: '"Activation maps"'
Publikováno v:
Frontiers in Microbiology, Vol 15 (2024)
IntroductionFast, accurate, and automatic analysis of histopathological images using digital image processing and deep learning technology is a necessary task. Conventional histopathological image analysis algorithms require the manual design of feat
Externí odkaz:
https://doaj.org/article/35063ea632b24388b7bd535d823940ee
Publikováno v:
Zeitschrift für Medizinische Physik, Vol 34, Iss 2, Pp 278-290 (2024)
Today, as in every life-threatening disease, early diagnosis of brain tumors plays a life-saving role. The brain tumor is formed by the transformation of brain cells from their normal structures into abnormal cell structures. These formed abnormal ce
Externí odkaz:
https://doaj.org/article/6e093f9bc8234eabb7d82073c602cb19
Autor:
B. Uma Maheswari, Dahlia Sam, Nitin Mittal, Abhishek Sharma, Sandeep Kaur, S. S. Askar, Mohamed Abouhawwash
Publikováno v:
BMC Medical Imaging, Vol 24, Iss 1, Pp 1-19 (2024)
Abstract Chest radiographs are examined in typical clinical settings by competent physicians for tuberculosis diagnosis. However, this procedure is time consuming and subjective. Due to the growing usage of machine learning techniques in applied scie
Externí odkaz:
https://doaj.org/article/cb467dbbea354466937219ee604bf962
Publikováno v:
IEEE Access, Vol 12, Pp 88829-88840 (2024)
Class activation maps (CAMs) are powerful tools for better understanding what convolutional neural networks learn and the reliability of their learning capability within relevant contexts. Highly inspired by Grad-CAM and Score-CAM, this manuscript pr
Externí odkaz:
https://doaj.org/article/bdab1e656ab74d2e8ce76cbefd468450
Publikováno v:
Frontiers in Plant Science, Vol 15 (2024)
This paper presents a robust deep learning method for fruit decay detection and plant identification. By addressing the limitations of previous studies that primarily focused on model accuracy, our approach aims to provide a more comprehensive soluti
Externí odkaz:
https://doaj.org/article/878de7c7df1a4bdd8c0f2062f6b16acb
Autor:
Muhammad Aasem, Muhammad Javed Iqbal
Publikováno v:
Frontiers in Big Data, Vol 7 (2024)
Chest X-ray (CXR) imaging is widely employed by radiologists to diagnose thoracic diseases. Recently, many deep learning techniques have been proposed as computer-aided diagnostic (CAD) tools to assist radiologists in minimizing the risk of incorrect
Externí odkaz:
https://doaj.org/article/ad4cd76e58c14cfb93a410915ab3497b
Autor:
Yueyue Han, Yingyan Huang, Hangcheng Dong, Fengdong Chen, Fa Zeng, Zhitao Peng, Qihua Zhu, Guodong Liu
Publikováno v:
High Power Laser Science and Engineering, Vol 12 (2024)
Segmenting dark-field images of laser-induced damage on large-aperture optics in high-power laser facilities is challenged by complicated damage morphology, uneven illumination and stray light interference. Fully supervised semantic segmentation algo
Externí odkaz:
https://doaj.org/article/2b6512670c84439eb78b4d9cf498c0d6
Publikováno v:
IEEE Access, Vol 11, Pp 50364-50381 (2023)
Diversity measures exploited by blind source separation (BSS) methods are usually based on either statistical attributes/geometrical structures or sparse/overcomplete (underdetermined) representations of the signals. This leads to some inefficient BS
Externí odkaz:
https://doaj.org/article/be195276b34a4dc4b720f08ff5d21c51
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 4963-4982 (2023)
Building change detection (BCD) from remote sensing images is essential in various practical applications. Recently, inspired by the achievement of deep learning in semantic segmentation (SS), methods that treat the BCD problem as a binary SS task us
Externí odkaz:
https://doaj.org/article/b3fe4d017fdc42f5bbc973fc82e1ae50
Autor:
Abdalla Ibrahim, Akshayaa Vaidyanathan, Sergey Primakov, Flore Belmans, Fabio Bottari, Turkey Refaee, Pierre Lovinfosse, Alexandre Jadoul, Celine Derwael, Fabian Hertel, Henry C. Woodruff, Helle D. Zacho, Sean Walsh, Wim Vos, Mariaelena Occhipinti, François-Xavier Hanin, Philippe Lambin, Felix M. Mottaghy, Roland Hustinx
Publikováno v:
Cancer Imaging, Vol 23, Iss 1, Pp 1-9 (2023)
Abstract Purpose Metastatic bone disease (MBD) is the most common form of metastases, most frequently deriving from prostate cancer. MBD is screened with bone scintigraphy (BS), which have high sensitivity but low specificity for the diagnosis of MBD
Externí odkaz:
https://doaj.org/article/c744ea8c106646c39197f1afa9bedf77