# Semi-Supervised Deep Subspace Embedding for Binary Classification of Sella Turcica
Initially, fine-tuning is employed to extract a set of discriminative features. Subsequently, deep subspace-based descriptors are applied for feature extraction and descriptor generation, transforming any given image into a non-linear subspace and leveraging manifold learning techniques. Following this, t-distributed stochastic neighbor embedding (t-SNE) is used for dimensionality reduction, with convergence criteria defined to stop the optimization process by minimizing the Kullback-Leibler (KL) divergence between probability distributions. This dimensionality reduction is critical for preserving the local structure of the data while simplifying the representation, making it easier to differentiate between bridging and non-bridging sella shapes. Next, a zero-shot classifier (ZsC) is integrated for binary classification using weighted binary cross-entropy loss, yielding the final output representing the dedicated class of sella structures and leveraging unlabeled data. Finally, the features computed in step two are used to train the Inception-ResNet-v2 network for classification purposes. The contributions of this study condense as follows:  
* The method proposed in this study introduces a novel approach for conducting a non-linear embedding with KL divergence for semi-supervised binary classification. * The proposed semi-supervised deep subspace embedding (SSLDSE) methodology creates a shift-invariant image representation by first producing an augmented image set for each input. This approach addresses the limitations of existing models, such as Active SSL and Contrastive SSL, which struggle with dataset variability and shifts. SSLDSE improves feature representation and robustness to transformations by mapping each image set to a non-linear subspace defined by a KL divergence point. * The model employs t-SNE for feature representations, utilizing manifold learning to capture non-linear structures that previous methods, such as GAN SSL and Contrastive SSL, may not effectively recognize. This enables SSLDSE to handle complex variations in medical images more effectively, improving classification performance by preserving essential relationships in the data. * SSLDSE also integrates a zero-shot classifier (ZsC) that leverages KL divergence loss to classify previously unseen or unlabeled ST structures. This is particularly important in medical imaging, where acquiring labeled data is challenging. Unlike GAN SSL and Active SSL, which require retraining to adapt to new data, SSLDSE generalizes to unseen classes without additional labeling, enhancing model adaptability and efficiency. * By combining real-time stochastic augmentation with KL divergence, SSLDSE generates shift-invariant features and generalizes better across different datasets, mitigating the risk of overfitting. In contrast, models like Active SSL, which may overfit with limited labeled data, show less consistency, while SSLDSE demonstrates accurate performance across diverse datasets. * The SSLDSE model is benchmarked against state-of-the-art semi-supervised learning models, including Active SSL, GAN SSL, and Contrastive SSL. Through experiments on both our proprietary dataset and the IEEE ISBI 2015 dataset, SSLDSE achieves superior performance in terms of accuracy, precision, recall, and F1 score. It outperforms these models by delivering robust classification with fewer labeled data, demonstrating its effectiveness in real-world applications. The approach was assessed against state-of-the-art SSL models using our proprietary dataset and the IEEE ISBI 2015 [25; 26] blind test dataset, and the outcomes showed promising performance. The outcomes indicated that the suggested deep relation-based semi-supervised binary-label classification approach surpasses the enhanced Inception-ResNet-v2 network in terms of performance.