The paper introduces MedNNS, a neural network search framework designed for medical imaging, addressing challenges in architecture selection and weight initialization. MedNNS constructs a meta-space encoding datasets and models based on their performance using a Supernetwork-based approach, expanding the model zoo size by 51x. The framework incorporates rank loss and Fréchet Inception Distance (FID) loss to capture inter-model and inter-dataset relationships, improving alignment in the meta-space and outperforming ImageNet pre-trained DL models and SOTA NAS methods.
This paper analyzes the impact of device uncertainties on deep neural networks (DNNs) in emerging device-based Computing-in-memory (CiM) systems. The authors propose UAE, an uncertainty-aware Neural Architecture Search scheme, to identify DNN models robust to these uncertainties. The goal is to mitigate accuracy drops when deploying trained models on real-world platforms.
This paper introduces a method for automatically designing convolutional neural network (CNN) architectures tailored for diabetic retinopathy (DR) diagnosis using fundus images. The approach uses k-medoid clustering, PCA, and inter/intra-class variations to optimize CNN depth and width. Validated on datasets including a local Saudi dataset and Kaggle benchmarks, the custom-designed models outperform pre-trained CNNs with fewer parameters.
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%.
The paper introduces the Prism Hypothesis, which posits a correspondence between an encoder's feature spectrum and its functional role, with semantic encoders capturing low-frequency components and pixel encoders retaining high-frequency information. Based on this, the authors propose Unified Autoencoding (UAE), a model that harmonizes semantic structure and pixel details using a frequency-band modulator. Experiments on ImageNet and MS-COCO demonstrate that UAE effectively unifies semantic abstraction and pixel-level fidelity, achieving state-of-the-art performance.
MBZUAI researchers have developed 'Byzantine antidote' (Bant), a novel defense mechanism against Byzantine attacks in federated learning. Bant uses trust scores and a trial function to dynamically filter and neutralize corrupted updates, even when a majority of nodes are compromised. The research was presented at the 40th Annual AAAI Conference on Artificial Intelligence.
Researchers have developed a CNN-based deep learning model for predicting coastal flooding in cities under various sea-level rise scenarios. The model utilizes a vision-based, low-resource DL framework and is trained on datasets from Abu Dhabi and San Francisco. Results show a 20% reduction in mean absolute error compared to existing methods, demonstrating potential for scalable coastal flood management.