Mitigating Overfitting in Medical Imaging: Self-Supervised Pretraining vs. ImageNet Transfer Learning for Dermatological Diagnosis
Fuente:
arXiv
Saved in:
| Main Authors: | Matas, Iván, Serrano, Carmen, Nogales, Miguel, Moreno, David, Ferrándiz, Lara, Ojeda, Teresa, Acha, Begoña |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Robust Melanoma Thickness Prediction via Deep Transfer Learning enhanced by XAI Techniques
by: Nogales, Miguel, et al.
Published: (2024)
by: Nogales, Miguel, et al.
Published: (2024)
ConvNet vs Transformer, Supervised vs CLIP: Beyond ImageNet Accuracy
by: Vishniakov, Kirill, et al.
Published: (2023)
by: Vishniakov, Kirill, et al.
Published: (2023)
Concordance in Basal Cell Carcinoma Diagnosis. Building a Proper Standard Reference to Train Artificial Intelligence Tools
by: Francisca Silva‐Clavería, et al.
Published: (2025)
by: Francisca Silva‐Clavería, et al.
Published: (2025)
Flaws of ImageNet, Computer Vision's Favourite Dataset
by: Kisel, Nikita, et al.
Published: (2024)
by: Kisel, Nikita, et al.
Published: (2024)
Rethinking Transfer Learning for Industrial Inspection: DINOv3 vs. ImageNet Pretraining Across RGB and X-ray Tasks
by: Gharbage, Mehdi, et al.
Published: (2026)
by: Gharbage, Mehdi, et al.
Published: (2026)
MultiTask Learning AI system to assist BCC diagnosis with dual explanation
by: Matas, Iván, et al.
Published: (2024)
by: Matas, Iván, et al.
Published: (2024)
Discriminating BCC Subtypes Using Entropy and Mutual Information from Dermoscopic Features
by: Matas, Iván, et al.
Published: (2025)
by: Matas, Iván, et al.
Published: (2025)
Concordance in basal cell carcinoma diagnosis. Building a proper ground truth to train Artificial Intelligence tools
by: Silva-Clavería, Francisca, et al.
Published: (2024)
by: Silva-Clavería, Francisca, et al.
Published: (2024)
Speedrunning ImageNet Diffusion
by: Bhanded, Swayam
Published: (2025)
by: Bhanded, Swayam
Published: (2025)
Infrared Object Detection with Ultra Small ConvNets: Is ImageNet Pretraining Still Useful?
by: Muralidharan, Srikanth, et al.
Published: (2025)
by: Muralidharan, Srikanth, et al.
Published: (2025)
Self-Supervised ImageNet Representations for In Vivo Confocal Microscopy: Tortuosity Grading without Segmentation Maps
by: Ouan, Kim, et al.
Published: (2026)
by: Ouan, Kim, et al.
Published: (2026)
Rethinking Domain‐Specific Pretraining by Supervised or Self‐Supervised Learning for Chest Radiograph Classification: A Comparative Study Against ImageNet Counterparts in Cold‐Start Active Learning
by: Han Yuan, et al.
Published: (2025)
by: Han Yuan, et al.
Published: (2025)
Transfer Learning from ImageNet for MEG-Based Decoding of Imagined Speech
by: Jhilal, Soufiane, et al.
Published: (2026)
by: Jhilal, Soufiane, et al.
Published: (2026)
Fine-Grained ImageNet Classification in the Wild
by: Lymperaiou, Maria, et al.
Published: (2023)
by: Lymperaiou, Maria, et al.
Published: (2023)
Automated Classification of Model Errors on ImageNet
by: Peychev, Momchil, et al.
Published: (2023)
by: Peychev, Momchil, et al.
Published: (2023)
ImageNot: A contrast with ImageNet preserves model rankings
by: Salaudeen, Olawale, et al.
Published: (2024)
by: Salaudeen, Olawale, et al.
Published: (2024)
Is Self-Supervised Pre-training on Satellite Imagery Better than ImageNet? A Systematic Study with Sentinel-2
by: Lahrichi, Saad, et al.
Published: (2025)
by: Lahrichi, Saad, et al.
Published: (2025)
Can Biases in ImageNet Models Explain Generalization?
by: Gavrikov, Paul, et al.
Published: (2024)
by: Gavrikov, Paul, et al.
Published: (2024)
What Makes ImageNet Look Unlike LAION
by: Shirali, Ali, et al.
Published: (2023)
by: Shirali, Ali, et al.
Published: (2023)
Toward Errorless Training ImageNet-1k
by: Deng, Bo, et al.
Published: (2025)
by: Deng, Bo, et al.
Published: (2025)
Comparative Analysis of ImageNet Pre-Trained Deep Learning Models and DINOv2 in Medical Imaging Classification
by: Huang, Yuning, et al.
Published: (2024)
by: Huang, Yuning, et al.
Published: (2024)
How far can we go with ImageNet for Text-to-Image generation?
by: Degeorge, L., et al.
Published: (2025)
by: Degeorge, L., et al.
Published: (2025)
Interpreting CLIP: Insights on the Robustness to ImageNet Distribution Shifts
by: Crabbé, Jonathan, et al.
Published: (2023)
by: Crabbé, Jonathan, et al.
Published: (2023)
What Values Do ImageNet-trained Classifiers Enact?
by: Penman, Will, et al.
Published: (2024)
by: Penman, Will, et al.
Published: (2024)
ImageNet-OOD: Deciphering Modern Out-of-Distribution Detection Algorithms
by: Yang, William, et al.
Published: (2023)
by: Yang, William, et al.
Published: (2023)
G3DR: Generative 3D Reconstruction in ImageNet
by: Reddy, Pradyumna, et al.
Published: (2024)
by: Reddy, Pradyumna, et al.
Published: (2024)
Babel-ImageNet: Massively Multilingual Evaluation of Vision-and-Language Representations
by: Geigle, Gregor, et al.
Published: (2023)
by: Geigle, Gregor, et al.
Published: (2023)
Accessing Vision Foundation Models via ImageNet-1K
by: Zhang, Yitian, et al.
Published: (2024)
by: Zhang, Yitian, et al.
Published: (2024)
SecONNds: Secure Outsourced Neural Network Inference on ImageNet
by: Balla, Shashank
Published: (2025)
by: Balla, Shashank
Published: (2025)
Swin-UMamba: Mamba-based UNet with ImageNet-based pretraining
by: Liu, Jiarun, et al.
Published: (2024)
by: Liu, Jiarun, et al.
Published: (2024)
Transfer or Self-Supervised? Bridging the Performance Gap in Medical Imaging
by: Zhao, Zehui, et al.
Published: (2024)
by: Zhao, Zehui, et al.
Published: (2024)
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?
by: Ozbulak, Utku, et al.
Published: (2025)
by: Ozbulak, Utku, et al.
Published: (2025)
ImageNet-D: Benchmarking Neural Network Robustness on Diffusion Synthetic Object
by: Zhang, Chenshuang, et al.
Published: (2024)
by: Zhang, Chenshuang, et al.
Published: (2024)
CNN and ViT Efficiency Study on Tiny ImageNet and DermaMNIST Datasets
by: Amangeldi, Aidar, et al.
Published: (2025)
by: Amangeldi, Aidar, et al.
Published: (2025)
Beyond ImageNet: Understanding Cross-Dataset Robustness of Lightweight Vision Models
by: Zhang, Weidong, et al.
Published: (2025)
by: Zhang, Weidong, et al.
Published: (2025)
Geometry aware 3D generation from in-the-wild images in ImageNet
by: Shen, Qijia, et al.
Published: (2024)
by: Shen, Qijia, et al.
Published: (2024)
Scaling Up Deep Clustering Methods Beyond ImageNet-1K
by: Adaloglou, Nikolas, et al.
Published: (2024)
by: Adaloglou, Nikolas, et al.
Published: (2024)
Self‐Supervised Transfer Learning of Cross‐Domains Histopathological Images for Cancer Diagnosis
by: Jianbo Zhu, et al.
Published: (2026)
by: Jianbo Zhu, et al.
Published: (2026)
SSPFormer: Self-Supervised Pretrained Transformer for MRI Images
by: Li, Jingkai, et al.
Published: (2026)
by: Li, Jingkai, et al.
Published: (2026)
MOAT: MobileNet‐Optimized Attention Transfer for Robust and Scalable Dermatology Image Classification
by: Pradeep Radhakrishnan, et al.
Published: (2025)
by: Pradeep Radhakrishnan, et al.
Published: (2025)
Similar Items
-
Robust Melanoma Thickness Prediction via Deep Transfer Learning enhanced by XAI Techniques
by: Nogales, Miguel, et al.
Published: (2024) -
ConvNet vs Transformer, Supervised vs CLIP: Beyond ImageNet Accuracy
by: Vishniakov, Kirill, et al.
Published: (2023) -
Concordance in Basal Cell Carcinoma Diagnosis. Building a Proper Standard Reference to Train Artificial Intelligence Tools
by: Francisca Silva‐Clavería, et al.
Published: (2025) -
Flaws of ImageNet, Computer Vision's Favourite Dataset
by: Kisel, Nikita, et al.
Published: (2024) -
Rethinking Transfer Learning for Industrial Inspection: DINOv3 vs. ImageNet Pretraining Across RGB and X-ray Tasks
by: Gharbage, Mehdi, et al.
Published: (2026)