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Found 5 papers, 1 papers with code
Solving the enigma: Deriving optimal explanations of deep networks
In this study, for the first time, we propose a novel framework designed to enhance the explainability of deep networks, by maximizing both the accuracy and the comprehensibility of the explanations.
Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge
The online validation set incorporated 52 HRCT scans from patients with fibrotic lung disease and the offline test set included 140 cases from fibrosis and COVID-19 patients.
An explainable three dimension framework to uncover learning patterns: A unified look in variable sulci recognition
With respect to this mathematical formulation, we propose a 3D explainability framework aimed at validating the outputs of deep learning networks in detecting the paracingulate sulcus an essential brain anatomical feature.
A novel framework employing deep multi-attention channels network for the autonomous detection of metastasizing cells through fluorescence microscopy
The method relies on fluorescence microscopy images showing the spatial organization of actin and vimentin filaments in normal and metastasizing single cells, using a combination of multi-attention channels network and global explainable techniques.
DenResCov-19: A deep transfer learning network for robust automatic classification of COVID-19, pneumonia, and tuberculosis from X-rays
The global pandemic of COVID-19 is continuing to have a significant effect on the well-being of global population, increasing the demand for rapid testing, diagnosis, and treatment.
