Less-Supervised Segmentation with Cnns: Scenarios, Models and Optimization
暫譯: 使用 CNN 的低監督分割:情境、模型與優化

Dolz, Jose, Ben Ayed, Ismail, Desrosiers, Christian

  • 出版商: Academic Press
  • 出版日期: 2025-09-16
  • 售價: $3,940
  • 貴賓價: 9.5$3,743
  • 語言: 英文
  • 頁數: 346
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 0323956742
  • ISBN-13: 9780323956741
  • 相關分類: 影像辨識 Image-recognition
  • 海外代購書籍(需單獨結帳)

相關主題

商品描述

Less-Supervised Segmentation with CNNs: Scenarios, Models and Optimization reviews recent progress in deep learning for image segmentation under scenarios with limited supervision, with a focus on medical imaging. The book presents main approaches and state-of-the-art models and includes a broad array of applications in medical image segmentation, including healthcare, oncology, cardiology and neuroimaging. A key objective is to make this mathematical subject accessible to a broad engineering and computing audience by using a large number of intuitive graphical illustrations. The emphasis is on giving conceptual understanding of the methods to foster easier learning.

This book is highly suitable for researchers and graduate students in computer vision, machine learning and medical imaging.

商品描述(中文翻譯)

以 CNN 進行較少監督的分割:情境、模型與優化》回顧了在有限監督情境下,深度學習在影像分割方面的最新進展,特別聚焦於醫學影像。該書介紹了主要的方法和最先進的模型,並涵蓋了醫學影像分割的廣泛應用,包括醫療保健、腫瘤學、心臟病學和神經影像學。其主要目標是通過使用大量直觀的圖形插圖,使這一數學主題對廣泛的工程和計算領域的讀者變得易於理解。重點在於提供方法的概念理解,以促進更輕鬆的學習。

本書非常適合計算機視覺、機器學習和醫學影像領域的研究人員和研究生。