Weakly Supervised Tumor Detection in PET Using Class Response for Treatment Outcome Prediction

Weakly Supervised Tumor Detection in PET Using Class Response for Treatment Outcome Prediction

It is proven that radiomic characteristics extracted from the tumor region are predictive. The first step in radiomic analysis is the segmentation of the lesion. However, this task is time consuming and requires a highly trained physician. This process could be automated using computer-aided detecti...

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Bibliographic Details
Main Authors: Amine Amyar, Romain Modzelewski, Pierre Vera, Vincent Morard, Su Ruan
Format: Article
Language:English
Published: MDPI AG 2022-05-01
Series:Journal of Imaging
Subjects:
weakly supervised learning
class activation maps
tumor detection
radiomics
image classification
image segmentation
Online Access:https://www.mdpi.com/2313-433X/8/5/130

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