التفاصيل البيبلوغرافية
| العنوان: |
Computational Evaluation of the Combination of Semi-Supervised and Active Learning for Histopathology Image Segmentation with Missing Annotations |
| المؤلفون: |
Laura Gálvez Jiménez, Dierckx, Lucile, Maxime Amodei, Hamed Razavi Khosroshahi, Natarajan Chidambaran, Anh-Thu Phan Ho, Alberto Franzin, ICCV CVAMD workshop |
| المساهمون: |
UCL - SST/ICTM/INGI - Pôle en ingénierie informatique |
| المصدر: |
Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, , p. 2552-2563 (2023) |
| سنة النشر: |
2023 |
| المجموعة: |
DIAL@USL-B (Université Saint-Louis, Bruxelles) |
| الوصف: |
Real-world segmentation tasks in digital pathology require a great effort from human experts to accurately annotate a sufficiently high number of images. Hence, there is a huge interest in methods that can make use of non-annotated samples, to alleviate the burden on the annotators. In this work, we evaluate two classes of such methods, semi-supervised and active learning, and their combination on a version of the GlaS dataset for gland segmentation in colorectal cancer tissue with missing annotations. Our results show that semi-supervised learning benefits from the combination with active learning and outperforms fully supervised learning on a dataset with missing annotations. However, an active learning procedure alone with a simple selection strategy obtains results of comparable quality. |
| نوع الوثيقة: |
conference object |
| اللغة: |
English |
| Relation: |
boreal:278524; http://hdl.handle.net/2078.1/278524 |
| الاتاحة: |
http://hdl.handle.net/2078.1/278524 |
| Rights: |
info:eu-repo/semantics/openAccess |
| رقم الانضمام: |
edsbas.B38FA91 |
| قاعدة البيانات: |
BASE |