3D Masked Modelling Advances Lesion Classification in Axial T2w Prostate MRI

• URL: http://arxiv.org/abs/2212.14267v1
• Date: Thu, 29 Dec 2022 11:32:49 GMT
• Title: 3D Masked Modelling Advances Lesion Classification in Axial T2w Prostate MRI
• Authors: Alvaro Fernandez-Quilez and Christoffer Gabrielsen Andersen and Trygve Eftest{\o}l and Svein Reidar Kjosavik and Ketil Oppedal
• Abstract summary: Masked Image Modelling (MIM) has been shown to be an efficient self-supervised learning (SSL) pre-training paradigm. We study MIM in the context of Prostate Cancer (PCa) lesion classification with T2 weighted (T2w) axial magnetic resonance imaging (MRI) data.
• Score: 0.125828876338076
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Masked Image Modelling (MIM) has been shown to be an efficient self-supervised learning (SSL) pre-training paradigm when paired with transformer architectures and in the presence of a large amount of unlabelled natural images. The combination of the difficulties in accessing and obtaining large amounts of labeled data and the availability of unlabelled data in the medical imaging domain makes MIM an interesting approach to advance deep learning (DL) applications based on 3D medical imaging data. Nevertheless, SSL and, in particular, MIM applications with medical imaging data are rather scarce and there is still uncertainty. around the potential of such a learning paradigm in the medical domain. We study MIM in the context of Prostate Cancer (PCa) lesion classification with T2 weighted (T2w) axial magnetic resonance imaging (MRI) data. In particular, we explore the effect of using MIM when coupled with convolutional neural networks (CNNs) under different conditions such as different masking strategies, obtaining better results in terms of AUC than other pre-training strategies like ImageNet weight initialization.

Related papers

• Unpaired Volumetric Harmonization of Brain MRI with Conditional Latent Diffusion [13.563413478006954]
  We propose a novel 3D MRI Harmonization framework through Conditional Latent Diffusion (HCLD) It comprises a generalizable 3D autoencoder that encodes and decodes MRIs through a 4D latent space. HCLD learns the latent distribution and generates harmonized MRIs with anatomical information from source MRIs while conditioned on target image style.
  arXiv  Detail & Related papers  (2024-08-18T00:13:48Z)
• Enhanced Self-supervised Learning for Multi-modality MRI Segmentation and Classification: A Novel Approach Avoiding Model Collapse [6.3467517115551875]
  Multi-modality magnetic resonance imaging (MRI) can provide complementary information for computer-aided diagnosis. Traditional deep learning algorithms are suitable for identifying specific anatomical structures segmenting lesions and classifying diseases with magnetic resonance images. Self-supervised learning (SSL) can effectively learn feature representations from unlabeled data by pre-training and is demonstrated to be effective in natural image analysis. Most SSL methods ignore the similarity of multi-modality MRI, leading to model collapse. We establish and validate a multi-modality MRI masked autoencoder consisting of hybrid mask pattern (HMP) and pyramid barlow twin (PBT
  arXiv  Detail & Related papers  (2024-07-15T01:11:30Z)
• MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentation [58.53672866662472]
  We introduce a modality-agnostic SAM adaptation framework, named as MA-SAM. Our method roots in the parameter-efficient fine-tuning strategy to update only a small portion of weight increments. By injecting a series of 3D adapters into the transformer blocks of the image encoder, our method enables the pre-trained 2D backbone to extract third-dimensional information from input data.
  arXiv  Detail & Related papers  (2023-09-16T02:41:53Z)
• Disruptive Autoencoders: Leveraging Low-level features for 3D Medical Image Pre-training [51.16994853817024]
  This work focuses on designing an effective pre-training framework for 3D radiology images. We introduce Disruptive Autoencoders, a pre-training framework that attempts to reconstruct the original image from disruptions created by a combination of local masking and low-level perturbations. The proposed pre-training framework is tested across multiple downstream tasks and achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2023-07-31T17:59:42Z)
• 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)
• SF2Former: Amyotrophic Lateral Sclerosis Identification From Multi-center MRI Data Using Spatial and Frequency Fusion Transformer [3.408266725482757]
  Amyotrophic Lateral Sclerosis (ALS) is a complex neurodegenerative disorder involving motor neuron degeneration. Deep learning has turned into a prominent class of machine learning programs in computer vision. This study introduces a framework named SF2Former that leverages vision transformer architecture's power to distinguish the ALS subjects from the control group.
  arXiv  Detail & Related papers  (2023-02-21T18:16:20Z)
• Interpretability Aware Model Training to Improve Robustness against Out-of-Distribution Magnetic Resonance Images in Alzheimer's Disease Classification [8.050897403457995]
  We propose an interpretability aware adversarial training regime to improve robustness against out-of-distribution samples originating from different MRI hardware. We present preliminary results showing promising performance on out-of-distribution samples.
  arXiv  Detail & Related papers  (2021-11-15T04:42:47Z)
• Modality Completion via Gaussian Process Prior Variational Autoencoders for Multi-Modal Glioma Segmentation [75.58395328700821]
  We propose a novel model, Multi-modal Gaussian Process Prior Variational Autoencoder (MGP-VAE), to impute one or more missing sub-modalities for a patient scan. MGP-VAE can leverage the Gaussian Process (GP) prior on the Variational Autoencoder (VAE) to utilize the subjects/patients and sub-modalities correlations. We show the applicability of MGP-VAE on brain tumor segmentation where either, two, or three of four sub-modalities may be missing.
  arXiv  Detail & Related papers  (2021-07-07T19:06:34Z)
• SAG-GAN: Semi-Supervised Attention-Guided GANs for Data Augmentation on Medical Images [47.35184075381965]
  We present a data augmentation method for generating synthetic medical images using cycle-consistency Generative Adversarial Networks (GANs) The proposed GANs-based model can generate a tumor image from a normal image, and in turn, it can also generate a normal image from a tumor image. We train the classification model using real images with classic data augmentation methods and classification models using synthetic images.
  arXiv  Detail & Related papers  (2020-11-15T14:01:24Z)
• Fed-Sim: Federated Simulation for Medical Imaging [131.56325440976207]
  We introduce a physics-driven generative approach that consists of two learnable neural modules. We show that our data synthesis framework improves the downstream segmentation performance on several datasets.
  arXiv  Detail & Related papers  (2020-09-01T19:17:46Z)

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.