TumorAwareNet: Deep representation learning with attention based sparse convolutional denoising autoencoder for brain tumor recognition
Learning discriminate representations from images plays crucial role in medical image analysis. The attention mechanism, on the other hand, leads to breakthrough results in the computer vision field by allowing models to provide varying levels of focus across image regions. In this work, we present...
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| Published in: | Multimedia tools and applications Vol. 83; no. 8; pp. 22099 - 22117 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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01.03.2024
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| ISSN: | 1573-7721, 1380-7501, 1573-7721 |
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| Abstract | Learning discriminate representations from images plays crucial role in medical image analysis. The attention mechanism, on the other hand, leads to breakthrough results in the computer vision field by allowing models to provide varying levels of focus across image regions. In this work, we present Tumor Aware Net, an end-to-end trainable attention based Convolutional Neural Network that learns effective representations from Magnetic Resonance (MR) images suitable for effective tumor recognition. The proposed model employs a Sparse Convolutional Denoising Autoencoder (SCDA) to project the higher dimensional MR image representations to a lower dimensional space with improved discrimination. These lower dimensional descriptors are passed through an attention module, which prioritizes tumor descriptors over the rest. Furthermore, the proposed SCDA is trained jointly with the Neural induced Support Vector Classifier (NSVC) to achieve maximum margin separation. The proposed model has been validated on several publicly available benchmark datasets for tumor recognition. Based on the outcomes of the experimental studies, we claim that the proposed model favours stability and complements the learned representations when combined with attention. Despite its simplicity in terms of model parameters, the proposed model outperforms existing models for tumor type categorization. |
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| AbstractList | Learning discriminate representations from images plays crucial role in medical image analysis. The attention mechanism, on the other hand, leads to breakthrough results in the computer vision field by allowing models to provide varying levels of focus across image regions. In this work, we present Tumor Aware Net, an end-to-end trainable attention based Convolutional Neural Network that learns effective representations from Magnetic Resonance (MR) images suitable for effective tumor recognition. The proposed model employs a Sparse Convolutional Denoising Autoencoder (SCDA) to project the higher dimensional MR image representations to a lower dimensional space with improved discrimination. These lower dimensional descriptors are passed through an attention module, which prioritizes tumor descriptors over the rest. Furthermore, the proposed SCDA is trained jointly with the Neural induced Support Vector Classifier (NSVC) to achieve maximum margin separation. The proposed model has been validated on several publicly available benchmark datasets for tumor recognition. Based on the outcomes of the experimental studies, we claim that the proposed model favours stability and complements the learned representations when combined with attention. Despite its simplicity in terms of model parameters, the proposed model outperforms existing models for tumor type categorization. |
| Author | Bodapati, Jyostna Devi Balaji, Bharadwaj Bagepalli |
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| CitedBy_id | crossref_primary_10_1016_j_bspc_2024_107221 crossref_primary_10_1016_j_compbiomed_2025_110166 crossref_primary_10_1007_s00138_025_01665_0 crossref_primary_10_3390_bioengineering12030274 crossref_primary_10_1007_s00521_023_09164_x crossref_primary_10_1016_j_neucom_2025_130824 |
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| Keywords | Reconstruction convolutional auto-encoder Pre-trained Convolutional Neural Networks (CNNs) Spatial attention Convolutional support vector classifier Brain tumor recognition Deep features Sparse Convolutional Denoising Autoencoder (SCDA) |
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