Enhanced Self-supervised Learning for Multi-modality MRI Segmentation and Classification: A Novel Approach Avoiding Model Collapse

• URL: http://arxiv.org/abs/2407.10377v2
• Date: Wed, 17 Jul 2024 07:05:57 GMT
• Title: Enhanced Self-supervised Learning for Multi-modality MRI Segmentation and Classification: A Novel Approach Avoiding Model Collapse
• Authors: Linxuan Han, Sa Xiao, Zimeng Li, Haidong Li, Xiuchao Zhao, Fumin Guo, Yeqing Han, Xin Zhou,
• Abstract summary: 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
• Score: 6.3467517115551875
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. However, manual labels are limited due to high expense, which hinders further improvement of model accuracy. 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. This limits the efficiency of pre-training, causing low accuracy in downstream segmentation and classification tasks. To solve this challenge, we establish and validate a multi-modality MRI masked autoencoder consisting of hybrid mask pattern (HMP) and pyramid barlow twin (PBT) module for SSL on multi-modality MRI analysis. The HMP concatenates three masking steps forcing the SSL to learn the semantic connections of multi-modality images by reconstructing the masking patches. We have proved that the proposed HMP can avoid model collapse. The PBT module exploits the pyramidal hierarchy of the network to construct barlow twin loss between masked and original views, aligning the semantic representations of image patches at different vision scales in latent space. Experiments on BraTS2023, PI-CAI, and lung gas MRI datasets further demonstrate the superiority of our framework over the state-of-the-art. The performance of the segmentation and classification is substantially enhanced, supporting the accurate detection of small lesion areas. The code is available at https://github.com/LinxuanHan/M2-MAE.

Related papers

• ContextMRI: Enhancing Compressed Sensing MRI through Metadata Conditioning [51.26601171361753]
  We propose ContextMRI, a text-conditioned diffusion model for MRI that integrates granular metadata into the reconstruction process. We show that increasing the fidelity of metadata, ranging from slice location and contrast to patient age, sex, and pathology, systematically boosts reconstruction performance.
  arXiv  Detail & Related papers  (2025-01-08T05:15:43Z)
• PMT: Progressive Mean Teacher via Exploring Temporal Consistency for Semi-Supervised Medical Image Segmentation [51.509573838103854]
  We propose a semi-supervised learning framework, termed Progressive Mean Teachers (PMT), for medical image segmentation. Our PMT generates high-fidelity pseudo labels by learning robust and diverse features in the training process. Experimental results on two datasets with different modalities, i.e., CT and MRI, demonstrate that our method outperforms the state-of-the-art medical image segmentation approaches.
  arXiv  Detail & Related papers  (2024-09-08T15:02:25Z)
• Multimodal Unlearnable Examples: Protecting Data against Multimodal Contrastive Learning [53.766434746801366]
  Multimodal contrastive learning (MCL) has shown remarkable advances in zero-shot classification by learning from millions of image-caption pairs crawled from the Internet. Hackers may unauthorizedly exploit image-text data for model training, potentially including personal and privacy-sensitive information. Recent works propose generating unlearnable examples by adding imperceptible perturbations to training images to build shortcuts for protection. We propose Multi-step Error Minimization (MEM), a novel optimization process for generating multimodal unlearnable examples.
  arXiv  Detail & Related papers  (2024-07-23T09:00:52Z)
• NeuroPictor: Refining fMRI-to-Image Reconstruction via Multi-individual Pretraining and Multi-level Modulation [55.51412454263856]
  This paper proposes to directly modulate the generation process of diffusion models using fMRI signals. By training with about 67,000 fMRI-image pairs from various individuals, our model enjoys superior fMRI-to-image decoding capacity.
  arXiv  Detail & Related papers  (2024-03-27T02:42:52Z)
• A Study of Dropout-Induced Modality Bias on Robustness to Missing Video Frames for Audio-Visual Speech Recognition [53.800937914403654]
  Advanced Audio-Visual Speech Recognition (AVSR) systems have been observed to be sensitive to missing video frames. While applying the dropout technique to the video modality enhances robustness to missing frames, it simultaneously results in a performance loss when dealing with complete data input. We propose a novel Multimodal Distribution Approximation with Knowledge Distillation (MDA-KD) framework to reduce over-reliance on the audio modality.
  arXiv  Detail & Related papers  (2024-03-07T06:06:55Z)
• Guided Reconstruction with Conditioned Diffusion Models for Unsupervised Anomaly Detection in Brain MRIs [35.46541584018842]
  Unsupervised Anomaly Detection (UAD) aims to identify any anomaly as an outlier from a healthy training distribution. generative models are used to learn the reconstruction of healthy brain anatomy for a given input image. We propose conditioning the denoising process of diffusion models with additional information derived from a latent representation of the input image.
  arXiv  Detail & Related papers  (2023-12-07T11:03:42Z)
• CoNeS: Conditional neural fields with shift modulation for multi-sequence MRI translation [5.662694302758443]
  Multi-sequence magnetic resonance imaging (MRI) has found wide applications in both modern clinical studies and deep learning research. It frequently occurs that one or more of the MRI sequences are missing due to different image acquisition protocols or contrast agent contraindications of patients. One promising approach is to leverage generative models to synthesize the missing sequences, which can serve as a surrogate acquisition.
  arXiv  Detail & Related papers  (2023-09-06T19:01:58Z)
• 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)
• Informative Data Selection with Uncertainty for Multi-modal Object Detection [25.602915381482468]
  We propose a universal uncertainty-aware multi-modal fusion model. Our model reduces the randomness in fusion and generates reliable output. Our fusion model is proven to resist severe noise interference like Gaussian, motion blur, and frost, with only slight degradation.
  arXiv  Detail & Related papers  (2023-04-23T16:36:13Z)
• Two-stage MR Image Segmentation Method for Brain Tumors based on Attention Mechanism [27.08977505280394]
  A coordination-spatial attention generation adversarial network (CASP-GAN) based on the cycle-consistent generative adversarial network (CycleGAN) is proposed. The performance of the generator is optimized by introducing the Coordinate Attention (CA) module and the Spatial Attention (SA) module. The ability to extract the structure information and the detailed information of the original medical image can help generate the desired image with higher quality.
  arXiv  Detail & Related papers  (2023-04-17T08:34:41Z)
• M$^{2}$SNet: Multi-scale in Multi-scale Subtraction Network for Medical Image Segmentation [73.10707675345253]
  We propose a general multi-scale in multi-scale subtraction network (M$2$SNet) to finish diverse segmentation from medical image. Our method performs favorably against most state-of-the-art methods under different evaluation metrics on eleven datasets of four different medical image segmentation tasks.
  arXiv  Detail & Related papers  (2023-03-20T06:26:49Z)
• M3AE: Multimodal Representation Learning for Brain Tumor Segmentation with Missing Modalities [29.455215925816187]
  Multimodal magnetic resonance imaging (MRI) provides complementary information for sub-region analysis of brain tumors. It is common to have one or more modalities missing due to image corruption, artifacts, acquisition protocols, allergy to contrast agents, or simply cost. We propose a novel two-stage framework for brain tumor segmentation with missing modalities.
  arXiv  Detail & Related papers  (2023-03-09T14:54:30Z)
• PCRLv2: A Unified Visual Information Preservation Framework for Self-supervised Pre-training in Medical Image Analysis [56.63327669853693]
  We propose to incorporate the task of pixel restoration for explicitly encoding more pixel-level information into high-level semantics. We also address the preservation of scale information, a powerful tool in aiding image understanding. The proposed unified SSL framework surpasses its self-supervised counterparts on various tasks.
  arXiv  Detail & Related papers  (2023-01-02T17:47:27Z)
• 3D Masked Modelling Advances Lesion Classification in Axial T2w Prostate MRI [0.125828876338076]
  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.
  arXiv  Detail & Related papers  (2022-12-29T11:32:49Z)
• Cascaded Multi-Modal Mixing Transformers for Alzheimer's Disease Classification with Incomplete Data [8.536869574065195]
  Multi-Modal Mixing Transformer (3MAT) is a disease classification transformer that not only leverages multi-modal data but also handles missing data scenarios. We propose a novel modality dropout mechanism to ensure an unprecedented level of modality independence and robustness to handle missing data scenarios.
  arXiv  Detail & Related papers  (2022-10-01T11:31:02Z)
• Mixed-UNet: Refined Class Activation Mapping for Weakly-Supervised Semantic Segmentation with Multi-scale Inference [28.409679398886304]
  We develop a novel model named Mixed-UNet, which has two parallel branches in the decoding phase. We evaluate the designed Mixed-UNet against several prevalent deep learning-based segmentation approaches on our dataset collected from the local hospital and public datasets.
  arXiv  Detail & Related papers  (2022-05-06T08:37:02Z)
• SMU-Net: Style matching U-Net for brain tumor segmentation with missing modalities [4.855689194518905]
  We propose a style matching U-Net (SMU-Net) for brain tumour segmentation on MRI images. Our co-training approach utilizes a content and style-matching mechanism to distill the informative features from the full-modality network into a missing modality network. Our style matching module adaptively recalibrates the representation space by learning a matching function to transfer the informative and textural features from a full-modality path into a missing-modality path.
  arXiv  Detail & Related papers  (2022-04-06T17:55:19Z)
• 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)
• Contrastive Model Inversion for Data-Free Knowledge Distillation [60.08025054715192]
  We propose Contrastive Model Inversion, where the data diversity is explicitly modeled as an optimizable objective. Our main observation is that, under the constraint of the same amount of data, higher data diversity usually indicates stronger instance discrimination. Experiments on CIFAR-10, CIFAR-100, and Tiny-ImageNet demonstrate that CMI achieves significantly superior performance when the generated data are used for knowledge distillation.
  arXiv  Detail & Related papers  (2021-05-18T15:13:00Z)
• Max-Fusion U-Net for Multi-Modal Pathology Segmentation with Attention and Dynamic Resampling [13.542898009730804]
  The performance of relevant algorithms is significantly affected by the proper fusion of the multi-modal information. We present the Max-Fusion U-Net that achieves improved pathology segmentation performance. We evaluate our methods using the Myocardial pathology segmentation (MyoPS) combining the multi-sequence CMR dataset.
  arXiv  Detail & Related papers  (2020-09-05T17:24:23Z)
• M2Net: Multi-modal Multi-channel Network for Overall Survival Time Prediction of Brain Tumor Patients [151.4352001822956]
  Early and accurate prediction of overall survival (OS) time can help to obtain better treatment planning for brain tumor patients. Existing prediction methods rely on radiomic features at the local lesion area of a magnetic resonance (MR) volume. We propose an end-to-end OS time prediction model; namely, Multi-modal Multi-channel Network (M2Net)
  arXiv  Detail & Related papers  (2020-06-01T05:21:37Z)

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.