Semi-Supervised Deep Subspace Embedding for Binary Classification of Sella Turcica
In orthodontics, the manual tracing of cephalometric radiographs is a common practice, where the Sella Turcica (ST) serves as a reference point. The radiologist often manually traces the outline of the sella using manual tools (e.g., calipers on radiographs). Perhaps the inherent complexity and vari...
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2024-11-01
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| author | Kaushlesh Singh Shakya Azadeh Alavi Julie Porteous Priti Khatri Amit Laddi Manojkumar Jaiswal Vinay Kumar |
| author_facet | Kaushlesh Singh Shakya Azadeh Alavi Julie Porteous Priti Khatri Amit Laddi Manojkumar Jaiswal Vinay Kumar |
| author_sort | Kaushlesh Singh Shakya |
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| description | In orthodontics, the manual tracing of cephalometric radiographs is a common practice, where the Sella Turcica (ST) serves as a reference point. The radiologist often manually traces the outline of the sella using manual tools (e.g., calipers on radiographs). Perhaps the inherent complexity and variability in the shapes of sella and the lack of advanced assessment tools make the classification of sella challenging, as it requires extensive training, skills, time, and manpower to detect subtle changes that often may not be apparent. Moreover, existing semi-supervised learning (SSL) methods face key limitations such as shift invariance, inadequate feature representation, overfitting on small datasets, and a lack of generalization to unseen variations in ST morphology. Medical imaging data are often unlabeled, limiting the training of automated classification systems for ST morphology. To address these limitations, a novel semi-supervised deep subspace embedding (SSLDSE) framework is proposed. This approach integrates real-time stochastic augmentation to significantly expand the training dataset and introduce natural variability in the ST morphology, overcoming the constraints of small and non-representative datasets. Non-linear features are extracted and mapped to a non-linear subspace using Kullback–Leibler divergence, which ensures that the model remains consistent despite image transformations, thus resolving issues related to shift invariance. Additionally, fine-tuning the Inception-ResNet-v2 network on these enriched features reduces retraining costs when new unlabeled data becomes available. t-distributed stochastic neighbor embedding (t-SNE) is employed for effective feature representation through manifold learning, capturing complex patterns that previous methods might miss. Finally, a zero-shot classifier is utilized to accurately categorize the ST, addressing the challenge of classifying new or unseen variations. Further, the proposed SSLDSE framework is evaluated through comparative analysis with the existing methods (Active SSL, GAN SSL, Contrastive SSL, Modified Inception-ResNet-v2) for ST classification using various evaluation metrics. The SSLDSE and the existing methods are trained on our dataset (sourced from PGI Chandigarh, India), and a blind test is conducted on the benchmark dataset (IEEE ISBI 2015). The proposed method improves classification accuracy by 15% compared to state-of-the-art models and reduces retraining costs. |
| format | Article |
| id | doaj-art-43e00d94dff146038123eadb3a1c7b08 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
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| spelling | doaj-art-43e00d94dff146038123eadb3a1c7b082024-12-13T16:22:59ZengMDPI AGApplied Sciences2076-34172024-11-0114231115410.3390/app142311154Semi-Supervised Deep Subspace Embedding for Binary Classification of Sella TurcicaKaushlesh Singh Shakya0Azadeh Alavi1Julie Porteous2Priti Khatri3Amit Laddi4Manojkumar Jaiswal5Vinay Kumar6School of Computing Technologies, RMIT University, Melbourne, VIC 3000, AustraliaSchool of Computing Technologies, RMIT University, Melbourne, VIC 3000, AustraliaSchool of Computing Technologies, RMIT University, Melbourne, VIC 3000, AustraliaAcademy of Scientific & Innovative Research (AcSIR), Ghaziabad 201002, IndiaAcademy of Scientific & Innovative Research (AcSIR), Ghaziabad 201002, IndiaOral Health Sciences Centre, Post Graduate Institute of Medical Education & Research (PGIMER), Chandigarh 160012, IndiaOral Health Sciences Centre, Post Graduate Institute of Medical Education & Research (PGIMER), Chandigarh 160012, IndiaIn orthodontics, the manual tracing of cephalometric radiographs is a common practice, where the Sella Turcica (ST) serves as a reference point. The radiologist often manually traces the outline of the sella using manual tools (e.g., calipers on radiographs). Perhaps the inherent complexity and variability in the shapes of sella and the lack of advanced assessment tools make the classification of sella challenging, as it requires extensive training, skills, time, and manpower to detect subtle changes that often may not be apparent. Moreover, existing semi-supervised learning (SSL) methods face key limitations such as shift invariance, inadequate feature representation, overfitting on small datasets, and a lack of generalization to unseen variations in ST morphology. Medical imaging data are often unlabeled, limiting the training of automated classification systems for ST morphology. To address these limitations, a novel semi-supervised deep subspace embedding (SSLDSE) framework is proposed. This approach integrates real-time stochastic augmentation to significantly expand the training dataset and introduce natural variability in the ST morphology, overcoming the constraints of small and non-representative datasets. Non-linear features are extracted and mapped to a non-linear subspace using Kullback–Leibler divergence, which ensures that the model remains consistent despite image transformations, thus resolving issues related to shift invariance. Additionally, fine-tuning the Inception-ResNet-v2 network on these enriched features reduces retraining costs when new unlabeled data becomes available. t-distributed stochastic neighbor embedding (t-SNE) is employed for effective feature representation through manifold learning, capturing complex patterns that previous methods might miss. Finally, a zero-shot classifier is utilized to accurately categorize the ST, addressing the challenge of classifying new or unseen variations. Further, the proposed SSLDSE framework is evaluated through comparative analysis with the existing methods (Active SSL, GAN SSL, Contrastive SSL, Modified Inception-ResNet-v2) for ST classification using various evaluation metrics. The SSLDSE and the existing methods are trained on our dataset (sourced from PGI Chandigarh, India), and a blind test is conducted on the benchmark dataset (IEEE ISBI 2015). The proposed method improves classification accuracy by 15% compared to state-of-the-art models and reduces retraining costs.https://www.mdpi.com/2076-3417/14/23/11154sella turcicadeep learningsemi supervised learningmanifold learningmedical imagesclassification |
| spellingShingle | Kaushlesh Singh Shakya Azadeh Alavi Julie Porteous Priti Khatri Amit Laddi Manojkumar Jaiswal Vinay Kumar Semi-Supervised Deep Subspace Embedding for Binary Classification of Sella Turcica Applied Sciences sella turcica deep learning semi supervised learning manifold learning medical images classification |
| title | Semi-Supervised Deep Subspace Embedding for Binary Classification of Sella Turcica |
| title_full | Semi-Supervised Deep Subspace Embedding for Binary Classification of Sella Turcica |
| title_fullStr | Semi-Supervised Deep Subspace Embedding for Binary Classification of Sella Turcica |
| title_full_unstemmed | Semi-Supervised Deep Subspace Embedding for Binary Classification of Sella Turcica |
| title_short | Semi-Supervised Deep Subspace Embedding for Binary Classification of Sella Turcica |
| title_sort | semi supervised deep subspace embedding for binary classification of sella turcica |
| topic | sella turcica deep learning semi supervised learning manifold learning medical images classification |
| url | https://www.mdpi.com/2076-3417/14/23/11154 |
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