BASIC: Bayesian Spiral Attention Classifier for Interpretable Medical Image Classification
Sagar, A.
Show abstract
Accurate medical image classification is critical for early diagnosis and effective treatment planning. However, conventional deep learning models often fail to provide reliable uncertainty estimates, limiting their clinical applicability. In this study, we propose a novel Bayesian neural network architecture for medical image classification that integrates channel-wise and spatial attention mechanisms, including Squeeze-and-Excitation (SE) blocks and a novel Spiral Attention, to enhance feature representation. The proposed model employs a Bayes-by-Backprop approach in the fully connected layers to quantify both epistemic and aleatoric uncertainties, allowing for reliable prediction confidence estimation. We validate our approach on multiple benchmark datasets, including diabetic retinopathy, COVID-19 chest X-rays, skin lesion images, and gastrointestinal endoscopy images. Extensive experiments demonstrate that our method not only achieves high classification performance but also provides meaningful uncertainty estimates, improving interpretability and robustness in clinical decision-making. Additionally, qualitative analysis using Grad-CAM visualizations highlights the models ability to focus on clinically relevant regions, further supporting its potential for real-world deployment.
Matching journals
The top 10 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Dual-Field Microvascular Segmentation: Hemodynamically-Consistent Attention Learning for Retinal Vasculature Mapping 96%
- SimSearch: A Human-in-the-Loop Learning Framework for Fast Detection of Regions of Interest in Microscopy Images 95%
- pathCLIP: Detection of Genes and Gene Relations from Biological Pathway Figures through Image-Text Contrastive Learning 93%
Similar papers in this journal
Similar papers in this journal
- Uncovering the effects of model initialization on deep model generalization: A study with adult and pediatric chest X-ray images 97%
- Enhancing Fairness in Disease Prediction by Optimizing Multiple Domain Adversarial Networks 95%
- Assessing generalizability of an AI-based visual test for cervical cancer screening 94%
Similar papers in this journal
- GLAPAL-H: Global, Local, And Parts Aware Learner for Hydrocephalus Infection Diagnosis in Low-Field MRI 97%
- Maximum Classifier Discrepancy Generative Adversarial Network for Jointly Harmonizing Scanner Effects and Improving Reproducibility of Downstream Tasks 94%
- Saak Transform-Based Machine Learning for Light-Sheet Imaging of Cardiac Trabeculation 94%
Similar papers in this journal
- STAMP: Simultaneous Training and Model Pruning for Low Data Regimes in Medical Image Segmentation 95%
- Clinical Validation of Saliency Maps for Understanding Deep Neural Networks in Ophthalmology 95%
- A Framework for Falsifiable Explanations of Machine Learning Models with an Application in Computational Pathology 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.