Data-Driven Clinical Decision-Making Using Deep Learning in Imaging

Name: Data-Driven Clinical Decision-Making Using Deep Learning in Imaging
Brand: Springer Nature
SKU: 9789819739660
Availability: InStock

Best Price (Coupon Required):
Buy Data-Driven Clinical Decision-Making Using Deep Learning in Imaging for $144.00 at @ Link.springer.com when you apply the 10% OFF coupon at checkout.
Click “Get Coupon & Buy” to copy the code and unlock the deal.

Set a price drop alert to never miss an offer.

1 Offer Price Range: $159.99 - $159.99

BEST PRICE

Single Product Purchase

$144.00

@ Link.springer.com with extra coupon

Price Comparison

Seller	Contact Seller	List Price	On Sale	Shipping	Best Promo	Final Price	Volume Discount	Financing	Availability	Seller's Page
BEST PRICE 1 Product Purchase @ Link.springer.com		$159.99	$159.99		10% OFF This deals requires coupon	$144.00		See Site	In stock	Visit Store

Product Details

Brand

Springer Nature

Manufacturer

N/A

Part Number

GTIN

9789819739660

Condition

New

Product Description

This book explores cutting-edge medical imaging advancements and their applications in clinical decision-making. The book contains various topics, methodologies, and applications, providing readers with a comprehensive understanding of the field's current state and prospects. It begins with exploring domain adaptation in medical imaging and evaluating the effectiveness of transfer learning to overcome challenges associated with limited labeled data. The subsequent chapters delve into specific applications, such as improving kidney lesion classification in CT scans, elevating breast cancer research through attention-based U-Net architecture for segmentation and classifying brain MRI images for neurological disorders. Furthermore, the book addresses the development of multimodal machine learning models for brain tumor prognosis, the identification of unique dermatological signatures using deep transfer learning, and the utilization of generative adversarial networks to enhance breast cancer detection systems by augmenting mammogram images. Additionally, the authors present a privacy-preserving approach for breast cancer risk prediction using federated learning, ensuring the confidentiality and security of sensitive patient data. This book brings together a global network of experts from various corners of the world, reflecting the truly international nature of its research.

Available Colors

N/A

Available Sizes

N/A