Fast interactive medical image segmentation with weakly supervised deep learning method

2020 ◽  
Vol 15 (9) ◽  
pp. 1437-1444
Author(s):  
Kibrom Berihu Girum ◽  
Gilles Créhange ◽  
Raabid Hussain ◽  
Alain Lalande
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Learning Method ◽  
Weakly Supervised

scholarly journals HPS-Net: Multi-Task Network for Medical Image Segmentation with Predictable Performance

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2107
Author(s):  
Xin Wei ◽  
Huan Wan ◽  
Fanghua Ye ◽  
Weidong Min
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Loss Function ◽  
Network Structure ◽  
Medical Diagnosis ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Training Mode ◽  
Cooperative Training

In recent years, medical image segmentation (MIS) has made a huge breakthrough due to the success of deep learning. However, the existing MIS algorithms still suffer from two types of uncertainties: (1) the uncertainty of the plausible segmentation hypotheses and (2) the uncertainty of segmentation performance. These two types of uncertainties affect the effectiveness of the MIS algorithm and then affect the reliability of medical diagnosis. Many studies have been done on the former but ignore the latter. Therefore, we proposed the hierarchical predictable segmentation network (HPS-Net), which consists of a new network structure, a new loss function, and a cooperative training mode. According to our knowledge, HPS-Net is the first network in the MIS area that can generate both the diverse segmentation hypotheses to avoid the uncertainty of the plausible segmentation hypotheses and the measure predictions about these hypotheses to avoid the uncertainty of segmentation performance. Extensive experiments were conducted on the LIDC-IDRI dataset and the ISIC2018 dataset. The results show that HPS-Net has the highest Dice score compared with the benchmark methods, which means it has the best segmentation performance. The results also confirmed that the proposed HPS-Net can effectively predict TNR and TPR.

A Study on Deep Learning Models for Medical Image Segmentation

2021 ◽  
pp. 161-174
Author(s):  
Pashupati Bhatt ◽  
Ashok Kumar Sahoo ◽  
Saumitra Chattopadhyay ◽  
Chandradeep Bhatt
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Learning Models

scholarly journals Robustness study of noisy annotation in deep learning based medical image segmentation

2020 ◽  
Vol 65 (17) ◽  
pp. 175007
Author(s):  
Shaode Yu ◽  
Mingli Chen ◽  
Erlei Zhang ◽  
Junjie Wu ◽  
Hang Yu ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Medical Image ◽  
Medical Image Segmentation

scholarly journals Intelligent Labeling Based on Fisher Information for Medical Image Segmentation Using Deep Learning

2019 ◽  
Vol 38 (11) ◽  
pp. 2642-2653 ◽  
Author(s):  
Jamshid Sourati ◽  
Ali Gholipour ◽  
Jennifer G. Dy ◽  
Xavier Tomas-Fernandez ◽  
Sila Kurugol ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Fisher Information ◽  
Medical Image ◽  
Medical Image Segmentation

A deep learning based medical image segmentation technique in Internet-of-Medical-Things domain

2020 ◽  
Vol 108 ◽  
pp. 135-144 ◽  
Author(s):  
Eric Ke Wang ◽  
Chien-Ming Chen ◽  
Mohammad Mehedi Hassan ◽  
Ahmad Almogren
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Segmentation Technique ◽  
Internet Of Medical Things

scholarly journals Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges

2019 ◽  
Vol 32 (4) ◽  
pp. 582-596 ◽  
Author(s):  
Mohammad Hesam Hesamian ◽  
Wenjing Jia ◽  
Xiangjian He ◽  
Paul Kennedy
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Learning Techniques
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