A novel hybrid convolutional and transformer network for lymphoma classification
Abstract Lymphoma poses a critical health challenge worldwide, demanding computer aided solutions towards diagnosis, treatment, and research to significantly enhance patient outcomes and combat this pervasive disease. Accurate classification of lymphoma subtypes from Whole Slide Images (WSIs) remain...
Saved in:
| Main Authors: | Mohamed Yacin Sikkandar, Sankar Ganesh Sundaram, Muteb Nasser Almeshari, S. Sabarunisha Begum, E. Siva Sankari, Yousef A. Alduraywish, Waeal J. Obidallah, Fahad Mansour Alotaibi |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-11277-3 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
SlidingConv: Domain-Specific Description of Sliding Discrete Cosine Transform Convolution for Halide
by: Yamato Kanetaka, et al.
Published: (2024-01-01) -
Pathological omics prediction of early and advanced colon cancer based on artificial intelligence model
by: Zhe Wang, et al.
Published: (2025-07-01) -
Development of neoplastic region selection algorithm based on breast cancer whole slide image
by: S. N. Rjabceva, et al.
Published: (2020-12-01) -
Impact of uniform illumination in widefield microscopy and mesoscopy
by: Mete Aslan, et al.
Published: (2025-07-01) -
Tumor segmentation in whole-slide histology images using deep learning
by: V. A. Kovalev, et al.
Published: (2019-06-01)