Search Results for author:
Found 3 papers, 1 papers with code
Interpretable Mammographic Image Classification using Case-Based Reasoning and Deep Learning
Compared to other methods, our model detects clinical features (mass margins) with equal or higher accuracy, provides a more detailed explanation of its prediction, and is better able to differentiate the classification-relevant parts of the image.
IAIA-BL: A Case-based Interpretable Deep Learning Model for Classification of Mass Lesions in Digital Mammography
Mammography poses important challenges that are not present in other computer vision tasks: datasets are small, confounding information is present, and it can be difficult even for a radiologist to decide between watchful waiting and biopsy based on a mammogram alone.
Mask Embedding in conditional GAN for Guided Synthesis of High Resolution Images
To use semantic masks as guidance whilst providing realistic synthesized results with fine details, we propose to use mask embedding mechanism to allow for a more efficient initial feature projection in the generator.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
