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- ArticleDecember 2024
Correlation Weighted Prototype-Based Self-supervised One-Shot Segmentation of Medical Images
AbstractMedical image segmentation is one of the domains where sufficient annotated data is not available. This necessitates the application of low-data frameworks like few-shot learning. Contemporary prototype-based frameworks often do not account for ...
- ArticleAugust 2023
SelfDocSeg: A Self-supervised Vision-Based Approach Towards Document Segmentation
- Subhajit Maity,
- Sanket Biswas,
- Siladittya Manna,
- Ayan Banerjee,
- Josep Lladós,
- Saumik Bhattacharya,
- Umapada Pal
Document Analysis and Recognition - ICDAR 2023Pages 342–360https://doi.org/10.1007/978-3-031-41676-7_20AbstractDocument layout analysis is a known problem to the documents research community and has been vastly explored yielding a multitude of solutions ranging from text mining, and recognition to graph-based representation, visual feature extraction, etc. ...
- rapid-communicationFebruary 2022
Self-supervised representation learning for detection of ACL tear injury in knee MR videos
Pattern Recognition Letters (PTRL), Volume 154, Issue CPages 37–43https://doi.org/10.1016/j.patrec.2022.01.008Highlights- Novel CNN model is proposed for efficiently solving jigsaw puzzle as pretext task.
The success of deep learning based models for computer vision applications requires large scale human annotated data which are often expensive to generate. Self-supervised learning, a subset of unsupervised learning, handles this ...
- research-articleDecember 2021
Interpretive self-supervised pre-training: boosting performance on visual medical data
ICVGIP '21: Proceedings of the Twelfth Indian Conference on Computer Vision, Graphics and Image ProcessingArticle No.: 15, Pages 1–9https://doi.org/10.1145/3490035.3490273Self-supervised learning algorithms have become one of the best tools for unsupervised representation learning. Although self- supervised algorithms have achieved state-of-the-art performance for classification tasks in the case of natural image data, ...
