Pre-trained deep learning (DL) models are increasingly accessible in public
repositories, i.e., model zoos. Given a new prediction task, finding the best
model to fine-tune can be computationally intensive and costly, especially when
the number of pre-trained models is large. Selecting the right pre-trained
models is crucial, yet complicated by the diversity of models from various
model families (like ResNet, Vit, Swin) and the hidden relationships between
models and datasets. Existing methods, which utilize basic information from
models and datasets to compute scores indicating model performance on target
datasets, overlook the intrinsic relationships, limiting their effectiveness in
model selection. In this study, we introduce TransferGraph, a novel framework
that reformulates model selection as a graph learning problem. TransferGraph
constructs a graph using extensive metadata extracted from models and datasets,
while capturing their inherent relationships. Through comprehensive experiments
across 16 real datasets, both images and texts, we demonstrate TransferGraph's
effectiveness in capturing essential model-dataset relationships, yielding up
to a 32% improvement in correlation between predicted performance and the
actual fine-tuning results compared to the state-of-the-art methods.

通过分析预先训练的深度学习模型的元数据，并捕捉模型和数据集之间的内在关系，研究人员提出了一种名为 TransferGraph 的新框架，将模型选择问题重新定义为图学习问题，并在 16 个真实数据集上进行了广泛实验，发现相较于现有方法，TransferGraph 在预测性能和实际微调结果之间的相关性上有 32% 的提升。

通过图学习的模型动物园进行模型选择

Model Selection with Model Zoo via Graph Learning

Diabetic Retinopathy (DR) stands as the leading cause of blindness globally,
particularly affecting individuals between the ages of 20 and 70. This paper
presents a Computer-Aided Diagnosis (CAD) system designed for the automatic
classification of retinal images into five distinct classes: Normal, Mild,
Moderate, Severe, and Proliferative Diabetic Retinopathy (PDR). The proposed
system leverages Convolutional Neural Networks (CNNs) employing pre-trained
deep learning models. Through the application of fine-tuning techniques, our
model is trained on fundus images of diabetic retinopathy with resolutions of
350x350x3 and 224x224x3. Experimental results obtained on the Kaggle platform,
utilizing resources comprising 4 CPUs, 17 GB RAM, and 1 GB Disk, demonstrate
the efficacy of our approach. The achieved Area Under the Curve (AUC) values
for CNN, MobileNet, VGG-16, InceptionV3, and InceptionResNetV2 models are 0.50,
0.70, 0.53, 0.63, and 0.69, respectively.

该研究提出了一种基于卷积神经网络和预训练深度学习模型的计算机辅助诊断系统，用于自动将视网膜图像分类为正常、轻度、中度、重度和增殖性糖尿病视网膜病变。实验结果表明，CNN、MobileNet、VGG-16、InceptionV3 和 InceptionResNetV2 模型的 AUC 值分别为 0.50、0.70、0.53、0.63 和 0.69。

使用预训练深度学习模型分类糖尿病视网膜病变

Classification of Diabetic Retinopathy using Pre-Trained Deep Learning  Models

Medical image analysis frequently encounters data scarcity challenges.
Transfer learning has been effective in addressing this issue while conserving
computational resources. The recent advent of foundational models like the
DINOv2, which uses the vision transformer architecture, has opened new
opportunities in the field and gathered significant interest. However, DINOv2's
performance on clinical data still needs to be verified. In this paper, we
performed a glioma grading task using three clinical modalities of brain MRI
data. We compared the performance of various pre-trained deep learning models,
including those based on ImageNet and DINOv2, in a transfer learning context.
Our focus was on understanding the impact of the freezing mechanism on
performance. We also validated our findings on three other types of public
datasets: chest radiography, fundus radiography, and dermoscopy. Our findings
indicate that in our clinical dataset, DINOv2's performance was not as strong
as ImageNet-based pre-trained models, whereas in public datasets, DINOv2
generally outperformed other models, especially when using the frozen
mechanism. Similar performance was observed with various sizes of DINOv2 models
across different tasks. In summary, DINOv2 is viable for medical image
classification tasks, particularly with data resembling natural images.
However, its effectiveness may vary with data that significantly differs from
natural images such as MRI. In addition, employing smaller versions of the
model can be adequate for medical task, offering resource-saving benefits. Our
codes are available at this https URL

DINOv2 对于临床数据的性能较 ImageNet 预训练模型较弱，但在公共数据集中表现出色，尤其是在使用冻结机制时。小型化的 DINOv2 模型在不同任务中表现相似，适用于医学图像分类任务并节省资源。