paperback. Condition: Very Good. Machine Learning in Medical Imaging: 10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, . (Lecture Notes in Computer Science, 11861) This book is in very good condition and will be shipped within 24 hours of ordering. The cover may have some limited signs of wear but the pages are clean, intact and the spine remains undamaged. This book has clearly been well maintained and looked after thus far. Money back guarantee if you are not satisfied. See all our books here, order more than 1 book and get discounted shipping. .
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paperback. Condition: Very Good. Shipped within 24 hours from our UK warehouse. Clean, undamaged book with no damage to pages and minimal wear to the cover. Spine still tight, in very good condition. Remember if you are not happy, you are covered by our 100% money back guarantee.
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Condition: As New. Unread book in perfect condition.
Condition: As New. Unread book in perfect condition.
Condition: As New. Unread book in perfect condition.
Language: English
Published by Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin, 2010
ISBN 10: 3642159885 ISBN 13: 9783642159886
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Paperback. Condition: new. Paperback. Prostatic adenocarcinoma (CAP) is the second most common malignancy with an estimated 190,000 new cases in the USA in 2010 (Source: American Cancer Society), and is the most frequently diagnosed cancer among men. If CAP is caught early, men have a high, five-year survival rate. Unfortunately there is no standardized ima- based screening protocol for early detection of CAP (unlike for breast cancers). In the USA high levels of prostate-specific antigen (PSA) warrant a trans-rectal ultrasound (TRUS) biopsy to enable histologic confirmation of presence or absence of CAP. With recent rapid developments in multi-parametric radiological imaging te- niques (spectroscopy, dynamic contrast enhanced MR imaging, PET, RF ultrasound), some of these functional and metabolic imaging modalities are allowing for definition of high resolution, multi-modal signatures for prostate cancer in vivo. Distinct com- tational and technological challenges for multi-modal data registration and classifi- tion still remain in leveraging this multi-parametric data for directing therapy and optimizing biopsy. Additionally, with the recent advent of whole slide digital sc- ners, digitized histopathology has become amenable to computerized image analysis. While it is known that outcome of prostate cancer (prognosis) is highly correlated with Gleason grade, pathologists often have difficulty in distinguishing between interme- ate Gleason grades from histopathology. Development of computerized image analysis methods for automated Gleason grading and predicting outcome on histopathology have to confront the significant computational challenges associated with working these very large digitized images. With recent rapid developments in multi-parametric radiological imaging te- niques (spectroscopy, dynamic contrast enhanced MR imaging, PET, RF ultrasound), some of these functional and metabolic imaging modalities are allowing for definition of high resolution, multi-modal signatures for prostate cancer in vivo. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Paperback. Condition: New.
Condition: New. pp. 290.
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