English
norskBlar i forfatter Artikler, rapporter og annet (fysikk og teknologi) "Salahuddin, Suaiba Amina"
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ADNet++: A few-shot learning framework for multi-class medical image volume segmentation with uncertainty-guided feature refinement
(Journal article; Tidsskriftartikkel, 2023-08-02)A major barrier to applying deep segmentation models in the medical domain is their typical data-hungry nature, requiring experts to collect and label large amounts of data for training. As a reaction, prototypical few-shot segmentation (FSS) models have recently gained traction as data-efficient alternatives. Nevertheless, despite the recent progress of these models, they still have some essential ... -
ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model
(Journal article; Tidsskriftartikkel; Peer reviewed, 2022-10-15)The need for interpretable models has fostered the development of self-explainable classifiers. Prior approaches are either based on multi-stage optimization schemes, impacting the predictive performance of the model, or produce explanations that are not transparent, trustworthy or do not capture the diversity of the data. To address these shortcomings, we propose ProtoVAE, a variational autoencoder-based ... -
A self-guided anomaly detection-inspired few-shot segmentation network
(Journal article; Tidsskriftartikkel; Peer reviewed, 2022-11-13)Standard strategies for fully supervised semantic segmentation of medical images require large pixel-level annotated datasets. This makes such methods challenging due to the manual labor required and limits the usability when segmentation is needed for new classes for which data is scarce. Few-shot segmentation (FSS) is a recent and promising direction within the deep learning literature designed ...
