Uncovering the effects of model initialization on deep model generalization: A study with adult and pediatric Chest X-ray images

• URL: http://arxiv.org/abs/2309.11318v1
• Date: Wed, 20 Sep 2023 13:42:48 GMT
• Title: Uncovering the effects of model initialization on deep model generalization: A study with adult and pediatric Chest X-ray images
• Authors: Sivaramakrishnan Rajaraman, Ghada Zamzmi, Feng Yang, Zhaohui Liang, Zhiyun Xue, and Sameer Antani
• Abstract summary: ImageNet-pretrained weights demonstrate superior generalizability over randomly counterparts, contradicting some findings for non-medical images. Weight-level ensembles of these models show significantly higher recall (p 0.05) during testing compared to individual models.
• Score: 5.454938535500864
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Model initialization techniques are vital for improving the performance and reliability of deep learning models in medical computer vision applications. While much literature exists on non-medical images, the impacts on medical images, particularly chest X-rays (CXRs) are less understood. Addressing this gap, our study explores three deep model initialization techniques: Cold-start, Warm-start, and Shrink and Perturb start, focusing on adult and pediatric populations. We specifically focus on scenarios with periodically arriving data for training, thereby embracing the real-world scenarios of ongoing data influx and the need for model updates. We evaluate these models for generalizability against external adult and pediatric CXR datasets. We also propose novel ensemble methods: F-score-weighted Sequential Least-Squares Quadratic Programming (F-SLSQP) and Attention-Guided Ensembles with Learnable Fuzzy Softmax to aggregate weight parameters from multiple models to capitalize on their collective knowledge and complementary representations. We perform statistical significance tests with 95% confidence intervals and p-values to analyze model performance. Our evaluations indicate models initialized with ImageNet-pre-trained weights demonstrate superior generalizability over randomly initialized counterparts, contradicting some findings for non-medical images. Notably, ImageNet-pretrained models exhibit consistent performance during internal and external testing across different training scenarios. Weight-level ensembles of these models show significantly higher recall (p<0.05) during testing compared to individual models. Thus, our study accentuates the benefits of ImageNet-pretrained weight initialization, especially when used with weight-level ensembles, for creating robust and generalizable deep learning solutions.

Related papers

• Fairness Analysis of CLIP-Based Foundation Models for X-Ray Image Classification [15.98427699337596]
  We perform a comprehensive fairness analysis of CLIP-like models applied to X-ray image classification. We assess their performance and fairness across diverse patient demographics and disease categories using zero-shot inference and various fine-tuning techniques.
  arXiv  Detail & Related papers  (2025-01-31T12:23:50Z)
• Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST [7.017817009055001]
  We study the capabilities of foundation models in medical image classification tasks by conducting a benchmark study on the MedMNIST dataset. 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.
  arXiv  Detail & Related papers  (2025-01-24T18:01:07Z)
• Latent Drifting in Diffusion Models for Counterfactual Medical Image Synthesis [55.959002385347645]
  Scaling by training on large datasets has been shown to enhance the quality and fidelity of image generation and manipulation with diffusion models. Latent Drifting enables diffusion models to be conditioned for medical images fitted for the complex task of counterfactual image generation. Our results demonstrate significant performance gains in various scenarios when combined with different fine-tuning schemes.
  arXiv  Detail & Related papers  (2024-12-30T01:59:34Z)
• Reinforcing Pre-trained Models Using Counterfactual Images [54.26310919385808]
  This paper proposes a novel framework to reinforce classification models using language-guided generated counterfactual images. We identify model weaknesses by testing the model using the counterfactual image dataset. We employ the counterfactual images as an augmented dataset to fine-tune and reinforce the classification model.
  arXiv  Detail & Related papers  (2024-06-19T08:07:14Z)
• Opinion-Unaware Blind Image Quality Assessment using Multi-Scale Deep Feature Statistics [54.08757792080732]
  We propose integrating deep features from pre-trained visual models with a statistical analysis model to achieve opinion-unaware BIQA (OU-BIQA) Our proposed model exhibits superior consistency with human visual perception compared to state-of-the-art BIQA models.
  arXiv  Detail & Related papers  (2024-05-29T06:09:34Z)
• Certification of Deep Learning Models for Medical Image Segmentation [44.177565298565966]
  We present for the first time a certified segmentation baseline for medical imaging based on randomized smoothing and diffusion models. Our results show that leveraging the power of denoising diffusion probabilistic models helps us overcome the limits of randomized smoothing.
  arXiv  Detail & Related papers  (2023-10-05T16:40:33Z)
• MedFMC: A Real-world Dataset and Benchmark For Foundation Model Adaptation in Medical Image Classification [41.16626194300303]
  Foundation models, often pre-trained with large-scale data, have achieved paramount success in jump-starting various vision and language applications. Recent advances further enable adapting foundation models in downstream tasks efficiently using only a few training samples. Yet, the application of such learning paradigms in medical image analysis remains scarce due to the shortage of publicly accessible data and benchmarks.
  arXiv  Detail & Related papers  (2023-06-16T01:46:07Z)
• Vision-Language Modelling For Radiological Imaging and Reports In The Low Data Regime [70.04389979779195]
  This paper explores training medical vision-language models (VLMs) where the visual and language inputs are embedded into a common space. We explore several candidate methods to improve low-data performance, including adapting generic pre-trained models to novel image and text domains. Using text-to-image retrieval as a benchmark, we evaluate the performance of these methods with variable sized training datasets of paired chest X-rays and radiological reports.
  arXiv  Detail & Related papers  (2023-03-30T18:20:00Z)
• Improved skin lesion recognition by a Self-Supervised Curricular Deep Learning approach [0.0]
  State-of-the-art deep learning approaches for skin lesion recognition often require pretraining on larger and more varied datasets. ImageNet is often used as the pretraining dataset, but its transferring potential is hindered by the domain gap between the source dataset and the target dermatoscopic scenario. In this work, we introduce a novel pretraining approach that sequentially trains a series of Self-Supervised Learning pretext tasks.
  arXiv  Detail & Related papers  (2021-12-22T17:45:47Z)
• A multi-stage machine learning model on diagnosis of esophageal manometry [50.591267188664666]
  The framework includes deep-learning models at the swallow-level stage and feature-based machine learning models at the study-level stage. This is the first artificial-intelligence-style model to automatically predict CC diagnosis of HRM study from raw multi-swallow data.
  arXiv  Detail & Related papers  (2021-06-25T20:09:23Z)
• 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.