{"product_id":"machine-learning-in-radiation-oncology-theory-and-applications","title":"Machine Learning in Radiation Oncology: Theory and Applications","description":"\u003csection class=\"book-about\"\u003e\u003csection class=\"book-editorial-section\"\u003e\u003ch2\u003eLikely Readers\u003c\/h2\u003e\n\u003cp\u003eThe natural reader is medicine readers, medical students, clinicians, and healthcare library buyers. A casual browser may miss the point; this is more useful when the buyer already has a course, rotation, certification, or clinical problem in mind.\u003c\/p\u003e\u003c\/section\u003e\u003csection class=\"book-editorial-section\"\u003e\u003ch2\u003eOverview\u003c\/h2\u003e\n\u003cp\u003eMachine Learning in Radiation Oncology: Theory and Applications reads as a specialty medicine reference built around oncology. That gives it a clearer role than a general survey text.\u003c\/p\u003e\n\u003cp\u003eThe strongest signal is applied reading: examples, cases, images, tracings, or clinical details that make the subject easier to work through in context.\u003c\/p\u003e\u003c\/section\u003e\u003csection class=\"book-editorial-section\"\u003e\u003ch2\u003eSubject Focus\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003eImages, tracings, or visual examples appear to be part of how the material is taught.\u003c\/li\u003e\n\u003cli\u003eOncology coverage keeps the book close to cancer diagnosis, treatment, or disease management.\u003c\/li\u003e\n\u003c\/ul\u003e\u003c\/section\u003e\u003csection class=\"book-editorial-section\"\u003e\u003ch2\u003eLearning Approach\u003c\/h2\u003e\n\u003cp\u003eA sensible reading path would start with oncology, then branch into the sections that answer the reader’s immediate clinical or academic question.\u003c\/p\u003e\u003c\/section\u003e\u003csection class=\"book-editorial-section\"\u003e\u003ch2\u003eEditorial Notes\u003c\/h2\u003e\n\u003cp\u003eVisual material matters here. Images, tracings, or illustrated examples can make the book more useful for pattern recognition and diagnostic comparison.\u003c\/p\u003e\u003c\/section\u003e\u003c\/section\u003e\u003csection class=\"book-details\"\u003e\u003ch2\u003eBook Details\u003c\/h2\u003e\n\u003ctable class=\"book-details-table\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003cth\u003eAuthor\u003c\/th\u003e\n\u003ctd\u003eIssam El Naqa, Ruijiang Li, Martin J. Murphy\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth\u003ePublisher\u003c\/th\u003e\n\u003ctd\u003eSpringer\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth\u003ePublication Date\u003c\/th\u003e\n\u003ctd\u003eOctober 12, 2016\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth\u003eLanguage\u003c\/th\u003e\n\u003ctd\u003eEnglish\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth\u003ePrint Length\u003c\/th\u003e\n\u003ctd\u003e350 pages\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth\u003eISBN-10\u003c\/th\u003e\n\u003ctd\u003e3319354647\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth\u003eISBN-13\u003c\/th\u003e\n\u003ctd\u003e978-3319354644\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth\u003eFormat\u003c\/th\u003e\n\u003ctd\u003eHardcover | Paperback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth\u003eDimensions\u003c\/th\u003e\n\u003ctd\u003e6.1 x 0.83 x 9.25 inches\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth\u003eItem Weight\u003c\/th\u003e\n\u003ctd\u003e13.16 pounds\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\u003c\/section\u003e","brand":"Springer","offers":[{"title":"Hardcover","offer_id":49654411034883,"sku":"9783319354644-hardcover","price":189.95,"currency_code":"USD","in_stock":true},{"title":"Paperback","offer_id":49654411067651,"sku":"9783319354644-paperback","price":99.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0823\/3884\/0835\/files\/61nS4O-b2wL.jpg?v=1789805800","url":"https:\/\/medlitx.com\/products\/machine-learning-in-radiation-oncology-theory-and-applications","provider":"MedLitx","version":"1.0","type":"link"}