Cargando…
Attention2majority: Weak multiple instance learning for regenerative kidney grading on whole slide images
Deep learning consistently demonstrates high performance in classifying and segmenting medical images like CT, PET, and MRI. However, compared to these kinds of images, whole slide images (WSIs) of stained tissue sections are huge and thus much less efficient to process, especially for deep learning...
Autores principales: | Su, Ziyu, Tavolara, Thomas E., Carreno-Galeano, Gabriel, Lee, Sang Jin, Gurcan, Metin N., Niazi, M.K.K. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10382794/ https://www.ncbi.nlm.nih.gov/pubmed/35512532 http://dx.doi.org/10.1016/j.media.2022.102462 |
Ejemplares similares
-
Contrastive Multiple Instance Learning: An Unsupervised Framework for Learning Slide-Level Representations of Whole Slide Histopathology Images without Labels
por: Tavolara, Thomas E., et al.
Publicado: (2022) -
Deep learning predicts gene expression as an intermediate data modality to identify susceptibility patterns in Mycobacterium tuberculosis infected Diversity Outbred mice
por: Tavolara, Thomas E., et al.
Publicado: (2021) -
NRK-ABMIL: Subtle Metastatic Deposits Detection for Predicting Lymph Node Metastasis in Breast Cancer Whole-Slide Images
por: Sajjad, Usama, et al.
Publicado: (2023) -
DeepFocus: Detection of out-of-focus regions in whole slide digital images using deep learning
por: Senaras, Caglar, et al.
Publicado: (2018) -
BCR-Net: A deep learning framework to predict breast cancer recurrence from histopathology images
por: Su, Ziyu, et al.
Publicado: (2023)