Comparison of fine-tuning strategies for transfer learning in medical image classification

• URL: http://arxiv.org/abs/2406.10050v1
• Date: Fri, 14 Jun 2024 14:00:02 GMT
• Title: Comparison of fine-tuning strategies for transfer learning in medical image classification
• Authors: Ana Davila, Jacinto Colan, Yasuhisa Hasegawa,
• Abstract summary: Despite availability of advanced pre-trained models, their direct application to medical imaging often falls short due to unique characteristics of medical data. This study provides a comprehensive analysis on the performance of various fine-tuning methods applied to pre-trained models across a spectrum of medical imaging domains.
• Score: 2.271776292902496
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the context of medical imaging and machine learning, one of the most pressing challenges is the effective adaptation of pre-trained models to specialized medical contexts. Despite the availability of advanced pre-trained models, their direct application to the highly specialized and diverse field of medical imaging often falls short due to the unique characteristics of medical data. This study provides a comprehensive analysis on the performance of various fine-tuning methods applied to pre-trained models across a spectrum of medical imaging domains, including X-ray, MRI, Histology, Dermoscopy, and Endoscopic surgery. We evaluated eight fine-tuning strategies, including standard techniques such as fine-tuning all layers or fine-tuning only the classifier layers, alongside methods such as gradually unfreezing layers, regularization based fine-tuning and adaptive learning rates. We selected three well-established CNN architectures (ResNet-50, DenseNet-121, and VGG-19) to cover a range of learning and feature extraction scenarios. Although our results indicate that the efficacy of these fine-tuning methods significantly varies depending on both the architecture and the medical imaging type, strategies such as combining Linear Probing with Full Fine-tuning resulted in notable improvements in over 50% of the evaluated cases, demonstrating general effectiveness across medical domains. Moreover, Auto-RGN, which dynamically adjusts learning rates, led to performance enhancements of up to 11% for specific modalities. Additionally, the DenseNet architecture showed more pronounced benefits from alternative fine-tuning approaches compared to traditional full fine-tuning. This work not only provides valuable insights for optimizing pre-trained models in medical image analysis but also suggests the potential for future research into more advanced architectures and fine-tuning methods.

Related papers

• Benchmarking Foundation Models and Parameter-Efficient Fine-Tuning for Prognosis Prediction in Medical Imaging [26.589728923739596]
  We evaluate and compare the transferability of Convolutional Neural Networks and Foundation Models in predicting clinical outcomes in COVID-19 patients.<n>The evaluations were conducted across multiple learning paradigms, including both extensive full-data scenarios and more clinically realistic Few-Shot Learning settings.
  arXiv  Detail & Related papers  (2025-06-23T09:16:04Z)
• Towards a general-purpose foundation model for fMRI analysis [58.06455456423138]
  We introduce NeuroSTORM, a framework that learns from 4D fMRI volumes and enables efficient knowledge transfer across diverse applications.<n>NeuroSTORM is pre-trained on 28.65 million fMRI frames (>9,000 hours) from over 50,000 subjects across multiple centers and ages 5 to 100.<n>It outperforms existing methods across five tasks: age/gender prediction, phenotype prediction, disease diagnosis, fMRI-to-image retrieval, and task-based fMRI.
  arXiv  Detail & Related papers  (2025-06-11T23:51:01Z)
• Enhancing Orthopox Image Classification Using Hybrid Machine Learning and Deep Learning Models [40.325359811289445]
  This paper uses Machine Learning models combined with pretrained Deep Learning models to extract deep feature representations without the need for augmented data.<n>The findings show that this feature extraction method, when paired with other methods in the state-of-the-art, produces excellent classification outcomes.
  arXiv  Detail & Related papers  (2025-06-06T11:52:07Z)
• Robust & Precise Knowledge Distillation-based Novel Context-Aware Predictor for Disease Detection in Brain and Gastrointestinal [5.001689778344014]
  We propose a novel framework that integrates Ant Colony Optimization for optimal teacher-student model selection and a novel context-aware predictor approach for temperature scaling.<n>The proposed framework is evaluated using three publicly available benchmark datasets.
  arXiv  Detail & Related papers  (2025-05-09T19:02:09Z)
• Embeddings are all you need! Achieving High Performance Medical Image Classification through Training-Free Embedding Analysis [0.0]
  Developing artificial intelligence (AI) and machine learning (ML) models for medical imaging typically involves extensive training and testing on large datasets. We investigated the feasibility of replacing conventional training procedures with an embedding-based approach.
  arXiv  Detail & Related papers  (2024-12-12T16:59:37Z)
• Disease Classification and Impact of Pretrained Deep Convolution Neural Networks on Diverse Medical Imaging Datasets across Imaging Modalities [0.0]
  This paper investigates the intricacies of using pretrained deep convolutional neural networks with transfer learning across diverse medical imaging datasets. It shows that the use of pretrained models as fixed feature extractors yields poor performance irrespective of the datasets. It is also found that deeper and more complex architectures did not necessarily result in the best performance.
  arXiv  Detail & Related papers  (2024-08-30T04:51:19Z)
• Benchmarking Retinal Blood Vessel Segmentation Models for Cross-Dataset and Cross-Disease Generalization [5.237321836999284]
  We train and evaluate five published models on the publicly available FIVES fundus image dataset. We find that image quality is a key factor determining segmentation outcomes.
  arXiv  Detail & Related papers  (2024-06-21T09:12:34Z)
• How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything Model [12.051904886550956]
  This work summarizes existing fine-tuning strategies with various backbone architectures, model components, and fine-tuning algorithms across 18 combinations. We evaluate them on 17 datasets covering all common radiology modalities. We release our code and MRI-specific fine-tuned weights, which consistently obtained superior performance over the original SAM.
  arXiv  Detail & Related papers  (2024-04-15T17:31:32Z)
• Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images [68.42215385041114]
  This paper introduces a novel lightweight multi-level adaptation and comparison framework to repurpose the CLIP model for medical anomaly detection. Our approach integrates multiple residual adapters into the pre-trained visual encoder, enabling a stepwise enhancement of visual features across different levels. Our experiments on medical anomaly detection benchmarks demonstrate that our method significantly surpasses current state-of-the-art models.
  arXiv  Detail & Related papers  (2024-03-19T09:28:19Z)
• DiCoM -- Diverse Concept Modeling towards Enhancing Generalizability in Chest X-Ray Studies [6.83819481805979]
  Chest X-Ray (CXR) is a widely used clinical imaging modality. Self-supervised pre-training has proven to outperform supervised pre-training in numerous downstream vision tasks. We introduce Diverse Concept Modeling (DiCoM), a novel self-supervised training paradigm.
  arXiv  Detail & Related papers  (2024-02-22T20:51:37Z)
• Learned Image resizing with efficient training (LRET) facilitates improved performance of large-scale digital histopathology image classification models [0.0]
  Histologic examination plays a crucial role in oncology research and diagnostics. Current approaches to training deep convolutional neural networks (DCNN) result in suboptimal model performance. We introduce a novel approach that addresses the main limitations of traditional histopathology classification model training.
  arXiv  Detail & Related papers  (2024-01-19T23:45:47Z)
• LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching [59.01894976615714]
  We introduce LVM-Med, the first family of deep networks trained on large-scale medical datasets. We have collected approximately 1.3 million medical images from 55 publicly available datasets. LVM-Med empirically outperforms a number of state-of-the-art supervised, self-supervised, and foundation models.
  arXiv  Detail & Related papers  (2023-06-20T22:21:34Z)
• Domain Generalization for Mammographic Image Analysis with Contrastive Learning [62.25104935889111]
  The training of an efficacious deep learning model requires large data with diverse styles and qualities. A novel contrastive learning is developed to equip the deep learning models with better style generalization capability. The proposed method has been evaluated extensively and rigorously with mammograms from various vendor style domains and several public datasets.
  arXiv  Detail & Related papers  (2023-04-20T11:40:21Z)
• Significantly improving zero-shot X-ray pathology classification via fine-tuning pre-trained image-text encoders [50.689585476660554]
  We propose a new fine-tuning strategy that includes positive-pair loss relaxation and random sentence sampling. Our approach consistently improves overall zero-shot pathology classification across four chest X-ray datasets and three pre-trained models.
  arXiv  Detail & Related papers  (2022-12-14T06:04:18Z)
• Robust and Efficient Medical Imaging with Self-Supervision [80.62711706785834]
  We present REMEDIS, a unified representation learning strategy to improve robustness and data-efficiency of medical imaging AI. We study a diverse range of medical imaging tasks and simulate three realistic application scenarios using retrospective data.
  arXiv  Detail & Related papers  (2022-05-19T17:34:18Z)
• On the Robustness of Pretraining and Self-Supervision for a Deep Learning-based Analysis of Diabetic Retinopathy [70.71457102672545]
  We compare the impact of different training procedures for diabetic retinopathy grading. We investigate different aspects such as quantitative performance, statistics of the learned feature representations, interpretability and robustness to image distortions. Our results indicate that models from ImageNet pretraining report a significant increase in performance, generalization and robustness to image distortions.
  arXiv  Detail & Related papers  (2021-06-25T08:32:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.