Aug 2026· International Conference on Circuit, Power and Computing Technologies· pp. 716-720· 0 citations· 8 references
Abstract
The problem of accurate identification of brain tumors using multi-modal MRIs still poses significant challenges due to tumor heterogeneity and variations across different image modes, as well as inconsistent availability of imaging modalities in real-world applications. Although deep learning algorithms such as convolution and transformer networks have demonstrated high efficacy in tumor segmentation through effective modeling of local and global contextual information, many state-of-the-art models perform directly on concatenated or fused images, making it difficult to leverage differences in information provided by different modalities and leading to inferior performance in cases of modality imbalance or missing data. To tackle the issue, we present in this paper a novel multimodal transformer network using the concept of reliability-driven modality attention for robust brain tumor segmentation. Our approach employs a feature extraction pipeline with a reliability estimator that automatically calculates weighting coefficients for each input modality (T1, T2, FLAIR, T1-CE), enabling more efficient feature representation than traditional fusion techniquesFurthermore, a slice-aware 2.5D context modeling strategy is used to capture inter-slice dependencies while keeping computational efficiency high compared to full 3D models. Extensive experiments on benchmark multi-modal MRI datasets show that the proposed approach achieves better segmentation performance than leading CNN, transformer, and hybrid methods, especially in scenarios with missing or degraded modalities. The results emphasize how reliability-aware fusion improves robustness, generalization, and clinical use of automated brain tumor analysis systems.
This study proposes a fully unsupervised brain tumor segmentation framework using multimodal MRI data that enables precise tumor delineation and is suitable for large-scale clinical integration.
J. Adlin, Arockia Selva Saroja· ITEGAM- Journal of Engineeri...· 0 citations
Brain tumor segmentation from Magnetic Resonance Imaging (MRI) is an important task in computer-aided diagnosis because accurate identification of tumor regions supports clinical assessment and treatment planning. However, the complex structure, irregular shape, intensity variation, and heterogeneous appearance of brain tumors make automated segmentation challenging. This study presents a comparative deep learning framework for brain tumor segmentation using the BraTS 2020 dataset and two-dimensional (2D) MRI images. In this study, we explore 2D deep learning architectures for automated tumor segmentation, focusing on U-Net, Vision Transformer (ViT), and Res-ViT models. U-Net, with its encoder–decoder design and skip connections, has been widely adopted for medical image segmentation due to its ability to capture fine-grained spatial features. ViT, leveraging self-attention mechanisms, introduces a global receptive field that enhances contextual understanding across slices. The Res-ViT hybrid combines residual learning with transformer-based attention, aiming to balance local feature extraction and long-range dependency modelling. Preprocessing steps, including skull stripping, intensity normalisation, and bias field correction, were applied to ensure consistency across scans. Data augmentation techniques such as rotation, flipping, and elastic deformation were employed to mitigate overfitting and improve generalisation. The models are evaluated using important segmentation metrics, including Intersection over Union (IoU), accuracy, precision, recall/sensitivity, loss, and 95th-percentile Hausdorff Distance (HD95). The comparative analysis aims to identify the strengths and limitations of convolutional and transformer-based approaches for 2D brain tumor segmentation. The study demonstrates the potential of combining local feature extraction and global contextual learning to achieve more accurate and robust brain tumor segmentation from multimodal MRI images.
Keywords: MRI; U-Net; ViT; BraTS 2020; IOU; HD95.
Lovedeep Kaur, Parminder Singh, Naveen Dhillon· International Journal of Com...· 0 citations
In clinical practice, Magnetic Resonance Imaging (MRI) data frequently suffer from the absence of certain imaging modalities, which inevitably degrades predictive performance. Existing approaches typically treat different modalities as independent and non-interacting during modal feature extraction, despite the presence of rich pixel-wise semantic correlations across modalities. To address this limitation, we propose Cross Modal Reliable Pixel Contrastive Learning (CMR-PCL), a novel framework for incomplete-modality brain tumor segmentation that aims to compensate for inter-modality information loss. Specifically, we introduce a label-inaccuracy–guided sampling strategy for each modality to identify and preserve reliable regions, thereby reducing the risk of noisy sample selection. We then enforce that reliable pixel embeddings belonging to the same semantic class are more similar to each other than to those from different classes. CMR-PCL adopts a standard training paradigm and imposes no additional architectural constraints, allowing it to be seamlessly integrated into existing incomplete-modality brain tumor segmentation frameworks. Extensive experiments on the BraTS2020, BraTS2018, and BraTS2015 datasets demonstrate that CMR-PCL consistently improves the performance of state-of-the-art methods.
A Multimodal Brain Tumour Classification and Segmentation framework based on a Dual-Attention Swin-UNet architecture, enhanced with a Quantum-Inspired Optimisation (QIO) technique, to overcome limitations in transformer-based medical imaging models.
Anirban Mondal, V. E. Jesi· DMPedia Lecture Notes in Com...· 0 citations
Brain tumor segmentation in magnetic resonance imaging (MRI) is a critical task for diagnosis and treatment planning. Despite the success of deep learning architectures such as U-Net and its variants, performance degradation across datasets remains a major challenge, particularly under domain shift and limited annotated data. To address this issue, this study systematically evaluates how individual MRI sequences influence model robustness across two well-known datasets. A ResUNet-based framework is employed, where each modality is trained independently to isolate its effect under a controlled cross-dataset evaluation protocol with tumor size stratification, without target-domain training, or with limited domain adaptation. Results show that the T2f/FLAIR sequence achieves the best cross-dataset performance, with Dice scores exceeding 75%. It consistently outperforms other modalities across most tumor size ranges, while multi-sequence training further improves performance. Additionally, even limited target-domain adaptation yields rapid initial gains, reducing the need for extensive annotations and costly retraining. Our source code is publicly available at https://github.com/henrique-zan/brain_tumor_segmentation/.
Henrique Zan Grande, João G. Pitol, Lucas B. Schuck et al.· 0 citations
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