Leveraging explainable AI and large-scale datasets for comprehensive classification of renal histologic types
Abstract Recently, as the number of cancer patients has increased, much research is being conducted for efficient treatment, including the use of artificial intelligence in genitourinary pathology. Recent research has focused largely on the classification of renal cell carcinoma subtypes. Nonetheles...
Saved in:
Main Authors: | Seung Wan Moon, Jisup Kim, Young Jae Kim, Sung Hyun Kim, Chi Sung An, Kwang Gi Kim, Chan Kwon Jung |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2025-01-01
|
Series: | Scientific Reports |
Subjects: | |
Online Access: | https://doi.org/10.1038/s41598-025-85857-8 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
MIPART: A Partial Decision Tree-Based Method for Multiple-Instance Classification
by: Kadriye Filiz Balbal
Published: (2024-12-01) -
Steganalysis based on transfer learning
by: Deng-pan YE, et al.
Published: (2017-01-01) -
SiameseNet based on multiple instance learning for accurate identification of the histological grade of ICC tumors
by: Zhizhan Fu, et al.
Published: (2025-02-01) -
Fully automated segmentation and classification of renal tumors on CT scans via machine learning
by: Jang Hee Han, et al.
Published: (2025-01-01) -
Leveraging paired mammogram views with deep learning for comprehensive breast cancer detection
by: Jae Won Seo, et al.
Published: (2025-02-01)