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Author: Samata Kakkad
Publisher: Frontiers Media SA
ISBN: 2832518443
Category : Medical
Languages : en
Pages : 172
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Book Description
Author: Samata Kakkad
Publisher: Frontiers Media SA
ISBN: 2832518443
Category : Medical
Languages : en
Pages : 172
Get Book Here
Book Description
Author: Samata Kakkad
Publisher: Frontiers Media SA
ISBN: 2832542506
Category : Medical
Languages : en
Pages : 141
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Book Description
We are delighted to present the inaugural Frontiers in Oncology "Women in Cancer Imaging and Image-directed Interventions” series of article collections. At present, less than 30% of researchers worldwide are women. Long-standing biases and gender stereotypes are discouraging girls and women away from science-related fields, and STEM research in particular. Science and gender equality are, however, essential to ensure sustainable development as highlighted by UNESCO. In order to change traditional mindsets, gender equality must be promoted, stereotypes defeated, and girls and women should be encouraged to pursue STEM careers.
Author: Omar Sultan Al-Kadi
Publisher: Frontiers Media SA
ISBN: 2832520618
Category : Medical
Languages : en
Pages : 200
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Book Description
Author: Guang Yang
Publisher: Frontiers Media SA
ISBN: 2832521630
Category : Medical
Languages : en
Pages : 107
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Author: Min Tang
Publisher: Frontiers Media SA
ISBN: 2832555020
Category : Medical
Languages : en
Pages : 145
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Book Description
Thanks to the development and deployment of whole-slide imaging technology in pathology, glass slides previously observed under a traditional microscope are now scanned and converted to digital images, which are more beneficial for remote access, portability, and ease of sharing to facilitate telepathology. More importantly, digitization of glass slides paves the way towards the wide use of artificial intelligence (AI) tools including machine/deep learning algorithms, resulting in improved diagnostic accuracy. In the past decade, a large number of studies have demonstrated the remarkable success of AI, particularly deep learning, in digital pathology, such as tumor region identification, metastasis detection, and patient prognosis. Differing from handcrafted feature-based approaches that take advantage of domain knowledge to delineate specific morphological measurements (e.g., nuclei shape and size and tissue texture) in the images as features for training, deep learning is a paradigm of feature learning entirely driven by the image data and/or labels. Herein, the use of deep learning in pathological diagnosis can not only handle increased workloads and expertise shortages but also obviate subjective diagnosis from pathologists. Yet there remain many scientific and technological challenges associated with the efficiency of deep learning algorithms for use in clinical practice. For example, deep learning requires a sufficient amount of training data for generalization and suffers from a lack of feature interpretability. The overarching goal of this special issue is to highlight novel research accomplishments and directions, related to advanced AI methodology development and applications in digital pathology.
Author: Yiyan Liu
Publisher: Frontiers Media SA
ISBN: 2832517366
Category : Medical
Languages : en
Pages : 188
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Author: Abhishek Mahajan
Publisher: Frontiers Media SA
ISBN: 2832518451
Category : Medical
Languages : en
Pages : 211
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Book Description
Author: Fu Wang
Publisher: Frontiers Media SA
ISBN: 2832511694
Category : Medical
Languages : en
Pages : 228
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Book Description
Author: Celestine Iwendi
Publisher: Frontiers Media SA
ISBN: 2832515754
Category : Medical
Languages : en
Pages : 1365
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Book Description
Author: Min Tang
Publisher: Frontiers Media SA
ISBN: 2832555012
Category : Science
Languages : en
Pages : 324
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Book Description
Our second Research Topic in this series, Computational tools in inferring cancer tissue-of-origin and molecular classification towards personalized cancer therapy, Volume II (https://fro.ntiers.in/14361) has over 8 accepted articles and further manuscripts currently under review. Due to the continued success of these Research Topics and the interest in the subject, we will launch a third volume on the same topic. Inferring cancer tissue-of-origin and molecular classification are two critical problems in personalized cancer therapy. It is known that there are about 5% cancers of unknown primary (CUP) site. These kinds of patients are under empirical chemotherapy, which leads to a very low survival rate. Thus, it is important to infer cancer tissue-of-origin. However, experimental methods usually fail to identify the exact tissue-of-origin even after the death of a patient, which provides a need for computational methods especially in the era of big biomedical data. Based on the finding that gene expressions of metastasis cancer cells are more similar to those of original tissue than metastasis tissue, there have been a few computational methods developed in this area. However, the accuracy of the methods is yet to be improved to assure a clinical usage. In addition to CUP, inferring cancer tissue-of-origin is also important in avoiding misdiagnosis even if the cancer origin is known.
