Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST
Foundation models are widely employed in medical image analysis, due to their high adaptability and generalizability for downstream tasks. With the increasing number of foundation models being released, model selection has become an important issue. In this work, we study the capabilities of foundation models in medical image classification tasks by conducting a benchmark study on the MedMNIST dataset. Specifically, we adopt various foundation models ranging from convolutional to Transformer-based models and implement both end-to-end training and linear probing for all classification tasks. The results demonstrate the significant potential of these pre-trained models when transferred for medical image classification. We further conduct experiments with different image sizes and various sizes of training data. By analyzing all the results, we provide preliminary, yet useful insights and conclusions on this topic.
Code (0)
등록된 구현이 없습니다.
Tasks
Classificationimage-classificationImage ClassificationMedical Image AnalysisMedical Image ClassificationModel SelectionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Text-guided Foundation Model Adaptation for Long-Tailed Medical Image Classification
In medical contexts, the imbalanced data distribution in long-tailed datasets, due to scarce labels for rare diseases, greatly impairs the diagnostic accuracy of deep learning models. Recent multimodal text-image supervi…
DiagnosticGPUimage-classificationImage Classification+2Lost in the Hype: Revealing and Dissecting the Performance Degradation of Medical Multimodal Large Language Models in Image Classification
The rise of multimodal large language models (MLLMs) has sparked an unprecedented wave of applications in the field of medical imaging analysis. However, as one of the earliest and most fundamental tasks integrated into …
Medical Image ClassificationAdvancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models
Using massive datasets, foundation models are large-scale, pre-trained models that perform a wide range of tasks. These models have shown consistently improved results with the introduction of new methods. It is crucial …
Classificationimage-classificationImage ClassificationMedical Image Classification+1Medical Report Generation Is A Multi-label Classification Problem
Medical report generation is a critical task in healthcare that involves the automatic creation of detailed and accurate descriptions from medical images. Traditionally, this task has been approached as a sequence genera…
Medical Report GenerationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONText-guided Foundation Model Adaptation for Pathological Image Classification
The recent surge of foundation models in computer vision and natural language processing opens up perspectives in utilizing multi-modal clinical data to train large models with strong generalizability. Yet pathological i…
Classificationimage-classificationImage Classificationtext annotation