A Distillation Approach to Transformer-Based Medical Image Classification with Limited Data
<b>Background/Objectives</b>: Although transformer-based deep learning architectures are preferred in many hybrid architectures due to their flexibility, they generally perform poorly on image classification tasks with small datasets. An important improvement in performance when transfor...
Saved in:
| Main Authors: | Aynur Sevinc, Murat Ucan, Buket Kaya |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/15/7/929 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Automated Dead Chicken Detection in Poultry Farms Using Knowledge Distillation and Vision Transformers
by: Ridip Khanal, et al.
Published: (2024-12-01) -
Hybrid Dual-Input Model for Respiratory Sound Classification With Mel Spectrogram and Waveform
by: Fan Wang, et al.
Published: (2025-01-01) -
Multi-stage framework using transformer models, feature fusion and ensemble learning for enhancing eye disease classification
by: Abdulaziz AlMohimeed
Published: (2025-08-01) -
Leveraging AI in ayurvedic agriculture: A RAG chatbot for comprehensive medicinal plant insights using hybrid deep learning approaches
by: Biplov Paneru, et al.
Published: (2024-12-01) -
An Inverted Residual Cross Head Knowledge Distillation Network for Remote Sensing Scene Image Classification
by: Cuiping Shi, et al.
Published: (2025-01-01)