BYOLMed3D: Self-Supervised Representation Learning of Medical Videos using Gradient Accumulation Assisted 3D BYOL Framework

• URL: http://arxiv.org/abs/2208.00444v1
• Date: Sun, 31 Jul 2022 14:48:06 GMT
• Title: BYOLMed3D: Self-Supervised Representation Learning of Medical Videos using Gradient Accumulation Assisted 3D BYOL Framework
• Authors: Siladittya Manna, Souvik Chakraborty
• Abstract summary: Supervised learning algorithms require a large volumes of balanced data to learn robust representations. Self-supervised learning algorithms are robust to imbalance in the data and are capable of learning robust representations. We train a 3D BYOL self-supervised model using gradient accumulation technique to deal with the large number of samples in a batch generally required in a self-supervised algorithm.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Applications on Medical Image Analysis suffer from acute shortage of large volume of data properly annotated by medical experts. Supervised Learning algorithms require a large volumes of balanced data to learn robust representations. Often supervised learning algorithms require various techniques to deal with imbalanced data. Self-supervised learning algorithms on the other hand are robust to imbalance in the data and are capable of learning robust representations. In this work, we train a 3D BYOL self-supervised model using gradient accumulation technique to deal with the large number of samples in a batch generally required in a self-supervised algorithm. To the best of our knowledge, this work is one of the first of its kind in this domain. We compare the results obtained through our experiments in the downstream task of ACL Tear Injury detection with the contemporary self-supervised pre-training methods and also with ResNet3D-18 initialized with the Kinetics-400 pre-trained weights. From the downstream task experiments, it is evident that the proposed framework outperforms the existing baselines.

Related papers

• Masked LoGoNet: Fast and Accurate 3D Image Analysis for Medical Domain [48.440691680864745]
  We introduce a new neural network architecture, termed LoGoNet, with a tailored self-supervised learning (SSL) method. LoGoNet integrates a novel feature extractor within a U-shaped architecture, leveraging Large Kernel Attention (LKA) and a dual encoding strategy. We propose a novel SSL method tailored for 3D images to compensate for the lack of large labeled datasets.
  arXiv  Detail & Related papers  (2024-02-09T05:06:58Z)
• 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)
• Data Efficient Contrastive Learning in Histopathology using Active Sampling [0.0]
  Deep learning algorithms can provide robust quantitative analysis in digital pathology. These algorithms require large amounts of annotated training data. Self-supervised methods have been proposed to learn features using ad-hoc pretext tasks. We propose a new method for actively sampling informative members from the training set using a small proxy network.
  arXiv  Detail & Related papers  (2023-03-28T18:51:22Z)
• Towards Robust Dataset Learning [90.2590325441068]
  We propose a principled, tri-level optimization to formulate the robust dataset learning problem. Under an abstraction model that characterizes robust vs. non-robust features, the proposed method provably learns a robust dataset.
  arXiv  Detail & Related papers  (2022-11-19T17:06:10Z)
• Self-supervised Model Based on Masked Autoencoders Advance CT Scans Classification [0.0]
  This paper is inspired by the self-supervised learning algorithm MAE. It uses the MAE model pre-trained on ImageNet to perform transfer learning on CT Scans dataset. This method improves the generalization performance of the model and avoids the risk of overfitting on small datasets.
  arXiv  Detail & Related papers  (2022-10-11T00:52:05Z)
• Advancing 3D Medical Image Analysis with Variable Dimension Transform based Supervised 3D Pre-training [45.90045513731704]
  This paper revisits an innovative yet simple fully-supervised 3D network pre-training framework. With a redesigned 3D network architecture, reformulated natural images are used to address the problem of data scarcity. Comprehensive experiments on four benchmark datasets demonstrate that the proposed pre-trained models can effectively accelerate convergence.
  arXiv  Detail & Related papers  (2022-01-05T03:11:21Z)
• Self Context and Shape Prior for Sensorless Freehand 3D Ultrasound Reconstruction [61.62191904755521]
  3D freehand US reconstruction is promising in addressing the problem by providing broad range and freeform scan. Existing deep learning based methods only focus on the basic cases of skill sequences. We propose a novel approach to sensorless freehand 3D US reconstruction considering the complex skill sequences.
  arXiv  Detail & Related papers  (2021-07-31T16:06:50Z)
• Gone Fishing: Neural Active Learning with Fisher Embeddings [55.08537975896764]
  There is an increasing need for active learning algorithms that are compatible with deep neural networks. This article introduces BAIT, a practical representation of tractable, and high-performing active learning algorithm for neural networks.
  arXiv  Detail & Related papers  (2021-06-17T17:26:31Z)
• Planar 3D Transfer Learning for End to End Unimodal MRI Unbalanced Data Segmentation [0.0]
  We present a novel approach of 2D to 3D transfer learning based on mapping pre-trained 2D convolutional neural network weights into planar 3D kernels. The method is validated by the proposed planar 3D res-u-net network with encoder transferred from the 2D VGG-16.
  arXiv  Detail & Related papers  (2020-11-23T17:11:50Z)
• 3D Self-Supervised Methods for Medical Imaging [7.65168530693281]
  We propose 3D versions for five different self-supervised methods, in the form of proxy tasks. Our methods facilitate neural network feature learning from unlabeled 3D images, aiming to reduce the required cost for expert annotation. The developed algorithms are 3D Contrastive Predictive Coding, 3D Rotation prediction, 3D Jigsaw puzzles, Relative 3D patch location, and 3D Exemplar networks.
  arXiv  Detail & Related papers  (2020-06-06T09:56:58Z)
• Self-Training with Improved Regularization for Sample-Efficient Chest X-Ray Classification [80.00316465793702]
  We present a deep learning framework that enables robust modeling in challenging scenarios. Our results show that using 85% lesser labeled data, we can build predictive models that match the performance of classifiers trained in a large-scale data setting.
  arXiv  Detail & Related papers  (2020-05-03T02:36:00Z)

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.