DCAT: Dual Cross-Attention Fusion for Disease Classification in Radiological Images with Uncertainty Estimation

Accurate and reliable image classification is crucial in radiology, where diagnostic decisions significantly impact patient outcomes. Conventional deep learning models tend to produce overconfident predictions despite underlying uncertainties, potentially leading to misdiagnoses. Attention mechanisms have emerged as powerful tools in deep learning, enabling models to focus on relevant parts of the input data. Combined with feature fusion, they can be effective in addressing uncertainty challenges. Cross-attention has become increasingly important in medical image analysis for capturing dependencies across features and modalities. This paper proposes a novel dual cross-attention fusion model for medical image analysis by addressing key challenges in feature integration and interpretability. Our approach introduces a bidirectional cross-attention mechanism with refined channel and spatial attention that dynamically fuses feature maps from EfficientNetB4 and ResNet34 leveraging multi-network contextual dependencies. The refined features through channel and spatial attention highlights discriminative patterns crucial for accurate classification. The proposed model achieved AUC of 99.75%, 100%, 99.93% and 98.69% and AUPR of 99.81%, 100%, 99.97%, and 96.36% on Covid-19, Tuberculosis, Pneumonia Chest X-ray images and Retinal OCT images respectively. The entropy values and several high uncertain samples give an interpretable visualization from the model enhancing transparency. By combining multi-scale feature extraction, bidirectional attention and uncertainty estimation, our proposed model strongly impacts medical image analysis.

Disease Classification and Impact of Pretrained Deep Convolution Neural Networks on Diverse Medical Imaging Datasets across Imaging Modalities

Imaging techniques such as Chest X-rays, whole slide images, and optical coherence tomography serve as the initial screening and detection for a wide variety of medical pulmonary and ophthalmic conditions respectively. This paper investigates the intricacies of using pretrained deep convolutional neural networks with transfer learning across diverse medical imaging datasets with varying modalities for binary and multiclass classification. We conducted a comprehensive performance analysis with ten network architectures and model families each with pretraining and random initialization. Our finding showed that the use of pretrained models as fixed feature extractors yields poor performance irrespective of the datasets. Contrary, histopathology microscopy whole slide images have better performance. It is also found that deeper and more complex architectures did not necessarily result in the best performance. This observation implies that the improvements in ImageNet are not parallel to the medical imaging tasks. Within a medical domain, the performance of the network architectures varies within model families with shifts in datasets. This indicates that the performance of models within a specific modality may not be conclusive for another modality within the same domain. This study provides a deeper understanding of the applications of deep learning techniques in medical imaging and highlights the impact of pretrained networks across different medical imaging datasets under five different experimental settings.