Feature learning using Stacked Autoencoder for Multimodal Fusion, Shared and Cross Learning on Medical Images

Islam, Zeeshan; Singh, Vikas; Verma, Nishchal K.

doi:10.1109/ibssc47189.2019.8973087

Cited by 1 publication

(1 citation statement)

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“…For instance, deep learning models using autoencoders have been proposed for feature extraction and image registration. Many researchers have focused on different models such as convolutional neural network (CNN), Restricted Boltzmann Machine (RBM), Deep Belief Network ((DBN), GAN, and Autoencoders to find the efficient solution for medical image fusion 82 .…”

Section: Deep Learning Methods For Image Fusionmentioning

confidence: 99%

Multimodal Classification on PET/CT Image Fusion for Lung Cancer: A Comprehensive Survey

Das

Chandra

2022

ECS Trans.

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Medical image fusion has become essential for accurate diagnosis. For example, a lung cancer diagnosis is currently conducted with the help of multimodality image fusion to find anatomical and functional information about the tumor and metabolic measurements to identify the lung cancer stage and metastatic information of the disease. Generally, the success of multimodality imaging for lung cancer diagnosis is due to the combination of PET and CT imaging advantages while minimizing their respective limitations. However, medical image fusion involves the registration of two different modalities, which is time-consuming and technically challenging, and it is a cause of concern in a clinical setting. Therefore, the paper's main objective is to identify the most efficient medical image fusion techniques and the recent advances by conducting a collective survey. In addition, the study delves into the impact of deep learning techniques for image fusion and their effectiveness in automating the image fusion procedure with better image quality while preserving essential clinical information.

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Section: Deep Learning Methods For Image Fusionmentioning

confidence: 99%