This paper explores language-independent alternatives to morphological segmentation for Arabic NLP using data-driven sub-word units, characters as a unit of learning, and word embeddings learned using a character CNN. The study evaluates these methods on machine translation and POS tagging tasks. Results show these methods achieve performance close to or surpassing state-of-the-art approaches. Why it matters: By offering simpler, more adaptable segmentation techniques, this research can help improve Arabic NLP applications across diverse domains and dialects.
This paper introduces a novel two-step method for predicting urban expansion using time-series satellite imagery. The approach combines semantic image segmentation with a CNN-LSTM model to learn temporal features. Experiments on satellite images from Riyadh, Jeddah, and Dammam in Saudi Arabia demonstrate improved performance compared to existing methods based on Mean Square Error, Root Mean Square Error, Peak Signal to Noise Ratio, Structural Similarity Index, and overall classification accuracy.
The study compares deep learning models trained via transfer learning from ImageNet (TII-models) against those trained solely on medical images (LMI-models) for disease segmentation. Results show that combining outputs from both model types can improve segmentation performance by up to 10% in certain scenarios. A repository of models, code, and over 10,000 medical images is available on GitHub to facilitate further research.
This paper introduces a self-supervised contrastive learning method for segmenting the left ventricle in echocardiography images when limited labeled data is available. The approach uses contrastive pretraining to improve the performance of UNet and DeepLabV3 segmentation networks. Experiments on the EchoNet-Dynamic dataset show the method achieves a Dice score of 0.9252, outperforming existing approaches, with code available on Github.
A new brain tumor segmentation method based on convolutional neural networks is proposed for the BraTS-GoAT challenge. The method employs the MedNeXt architecture and model ensembling to segment tumors in brain MRI scans from diverse populations. Experiments on the unseen validation set demonstrate promising results with an average DSC of 85.54%.
Researchers from MBZUAI introduced RP-SAM2, a method to improve surgical instrument segmentation by refining point prompts for more stable results. RP-SAM2 uses a novel shift block and compound loss function to reduce sensitivity to point prompt placement, improving segmentation accuracy in data-constrained settings. Experiments on the Cataract1k and CaDIS datasets show that RP-SAM2 enhances segmentation accuracy and reduces variance compared to SAM2, with code available on GitHub.
Researchers from MBZUAI have developed MMRINet, a Mamba-based neural network for efficient brain tumor segmentation in MRI scans. The model uses Dual-Path Feature Refinement and Progressive Feature Aggregation to achieve high accuracy with only 2.5M parameters, making it suitable for low-resource clinical environments. MMRINet achieves a Dice score of 0.752 and HD95 of 12.23 on the BraTS-Lighthouse SSA 2025 benchmark.