Weakly supervised machine learning
Abstract Supervised learning aims to build a function or model that seeks as many mappings as possible between the training data and outputs, where each training data will predict as a label to match its corresponding ground‐truth value. Although supervised learning has achieved great success in man...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-09-01
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| Series: | CAAI Transactions on Intelligence Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/cit2.12216 |
| _version_ | 1797682917430788096 |
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| author | Zeyu Ren Shuihua Wang Yudong Zhang |
| author_facet | Zeyu Ren Shuihua Wang Yudong Zhang |
| author_sort | Zeyu Ren |
| collection | DOAJ |
| description | Abstract Supervised learning aims to build a function or model that seeks as many mappings as possible between the training data and outputs, where each training data will predict as a label to match its corresponding ground‐truth value. Although supervised learning has achieved great success in many tasks, sufficient data supervision for labels is not accessible in many domains because accurate data labelling is costly and laborious, particularly in medical image analysis. The cost of the dataset with ground‐truth labels is much higher than in other domains. Therefore, it is noteworthy to focus on weakly supervised learning for medical image analysis, as it is more applicable for practical applications. In this review, the authors give an overview of the latest process of weakly supervised learning in medical image analysis, including incomplete, inexact, and inaccurate supervision, and introduce the related works on different applications for medical image analysis. Related concepts are illustrated to help readers get an overview ranging from supervised to unsupervised learning within the scope of machine learning. Furthermore, the challenges and future works of weakly supervised learning in medical image analysis are discussed. |
| first_indexed | 2024-03-12T00:06:51Z |
| format | Article |
| id | doaj.art-111c8769c4be45d4a23a30085df712b8 |
| institution | Directory Open Access Journal |
| issn | 2468-2322 |
| language | English |
| last_indexed | 2024-03-12T00:06:51Z |
| publishDate | 2023-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | CAAI Transactions on Intelligence Technology |
| spelling | doaj.art-111c8769c4be45d4a23a30085df712b82023-09-16T16:19:34ZengWileyCAAI Transactions on Intelligence Technology2468-23222023-09-018354958010.1049/cit2.12216Weakly supervised machine learningZeyu Ren0Shuihua Wang1Yudong Zhang2School of Computing and Mathematical Sciences University of Leicester Leicester UKSchool of Computing and Mathematical Sciences University of Leicester Leicester UKSchool of Computing and Mathematical Sciences University of Leicester Leicester UKAbstract Supervised learning aims to build a function or model that seeks as many mappings as possible between the training data and outputs, where each training data will predict as a label to match its corresponding ground‐truth value. Although supervised learning has achieved great success in many tasks, sufficient data supervision for labels is not accessible in many domains because accurate data labelling is costly and laborious, particularly in medical image analysis. The cost of the dataset with ground‐truth labels is much higher than in other domains. Therefore, it is noteworthy to focus on weakly supervised learning for medical image analysis, as it is more applicable for practical applications. In this review, the authors give an overview of the latest process of weakly supervised learning in medical image analysis, including incomplete, inexact, and inaccurate supervision, and introduce the related works on different applications for medical image analysis. Related concepts are illustrated to help readers get an overview ranging from supervised to unsupervised learning within the scope of machine learning. Furthermore, the challenges and future works of weakly supervised learning in medical image analysis are discussed.https://doi.org/10.1049/cit2.12216deep learningunsupervised learning |
| spellingShingle | Zeyu Ren Shuihua Wang Yudong Zhang Weakly supervised machine learning CAAI Transactions on Intelligence Technology deep learning unsupervised learning |
| title | Weakly supervised machine learning |
| title_full | Weakly supervised machine learning |
| title_fullStr | Weakly supervised machine learning |
| title_full_unstemmed | Weakly supervised machine learning |
| title_short | Weakly supervised machine learning |
| title_sort | weakly supervised machine learning |
| topic | deep learning unsupervised learning |
| url | https://doi.org/10.1049/cit2.12216 |
| work_keys_str_mv | AT zeyuren weaklysupervisedmachinelearning AT shuihuawang weaklysupervisedmachinelearning AT yudongzhang weaklysupervisedmachinelearning |