Neural Pre-Processing: A Learning Framework for End-to-end Brain MRI Pre-processing

• URL: http://arxiv.org/abs/2303.12148v1
• Date: Tue, 21 Mar 2023 19:10:21 GMT
• Title: Neural Pre-Processing: A Learning Framework for End-to-end Brain MRI Pre-processing
• Abstract summary: Head MRI pre-processing involves converting raw images to an intensity-normalized, skull-stripped brain in a standard coordinate space. We propose an end-to-end weakly supervised learning approach, called Neural Pre-processing (NPP), for solving all three sub-tasks simultaneously via a neural network.
• Score: 13.195381426141731
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Head MRI pre-processing involves converting raw images to an intensity-normalized, skull-stripped brain in a standard coordinate space. In this paper, we propose an end-to-end weakly supervised learning approach, called Neural Pre-processing (NPP), for solving all three sub-tasks simultaneously via a neural network, trained on a large dataset without individual sub-task supervision. Because the overall objective is highly under-constrained, we explicitly disentangle geometric-preserving intensity mapping (skull-stripping and intensity normalization) and spatial transformation (spatial normalization). Quantitative results show that our model outperforms state-of-the-art methods which tackle only a single sub-task. Our ablation experiments demonstrate the importance of the architecture design we chose for NPP. Furthermore, NPP affords the user the flexibility to control each of these tasks at inference time. The code and model are freely-available at \url{https://github.com/Novestars/Neural-Pre-processing}.

Related papers

• Probe-EM: Targeted Neuron Tracing via Training-Free Semantic Verification [13.282533076475383]
  We propose a training-free, targeted neuron tracing framework for processing terabyte- or petabyte-scale electron microscopy data.<n>Specifically, we introduce a skeleton-guided Heuristic Spatial Search paradigm that leverages geometric priors to iteratively reconstruct neuronal morphologies.<n>We further develop a Dimension-Aware Semantic Verification strategy built upon the foundation model NeuroSAM 2.
  arXiv  Detail & Related papers  (2026-07-06T05:56:26Z)
• BrainRVQ: A High-Fidelity EEG Foundation Model via Dual-Domain Residual Quantization and Hierarchical Autoregression [26.114257185901838]
  We propose BrainRVQ, a general-purpose EEG foundation model pre-trained on a large-scale corpus of clinical EEG data.<n>BrainRVQ features a Dual-Domain Residual Vector Quantization (DD-RVQ) tokenizer that disentangles temporal waveforms and spectral patterns into hierarchical discrete codes.
  arXiv  Detail & Related papers  (2026-02-18T23:30:36Z)
• RPNT: Robust Pre-trained Neural Transformer -- A Pathway for Generalized Motor Decoding [12.246453522508341]
  We propose RPNT - Robust Pretrained Neural Transformer - to achieve robust generalization through pretraining.<n> RPNT unique components include 1) Multidimensional rotary positional embedding (MRoPE) to aggregate experimental metadata such as site coordinates, session name and behavior types; 2) Context-based attention mechanism via convolution kernels operating on global attention to learn local temporal structures for handling non-stationarity of neural population activity; 3) Robust self-supervised learning (SSL) objective with uniform causal masking strategies and contrastive representations.<n>Our results show that RPNT consistently achieves and surpasses the decoding performance of existing decoding models in all tasks.
  arXiv  Detail & Related papers  (2026-01-25T01:18:25Z)
• Moving Beyond Diffusion: Hierarchy-to-Hierarchy Autoregression for fMRI-to-Image Reconstruction [65.67001243986981]
  We propose MindHier, a coarse-to-fine fMRI-to-image reconstruction framework built on scale-wise autoregressive modeling.<n>MindHier achieves superior semantic fidelity, 4.67x faster inference, and more deterministic results than the diffusion-based baselines.
  arXiv  Detail & Related papers  (2025-10-25T15:40:07Z)
• Principled Approaches for Extending Neural Architectures to Function Spaces for Operator Learning [78.88684753303794]
  Deep learning has predominantly advanced through applications in computer vision and natural language processing.<n>Neural operators are a principled way to generalize neural networks to mappings between function spaces.<n>This paper identifies and distills the key principles for constructing practical implementations of mappings between infinite-dimensional function spaces.
  arXiv  Detail & Related papers  (2025-06-12T17:59:31Z)
• N-BVH: Neural ray queries with bounding volume hierarchies [51.430495562430565]
  In 3D computer graphics, the bulk of a scene's memory usage is due to polygons and textures. We devise N-BVH, a neural compression architecture designed to answer arbitrary ray queries in 3D. Our method provides faithful approximations of visibility, depth, and appearance attributes.
  arXiv  Detail & Related papers  (2024-05-25T13:54:34Z)
• See Through Their Minds: Learning Transferable Neural Representation from Cross-Subject fMRI [32.40827290083577]
  Deciphering visual content from functional Magnetic Resonance Imaging (fMRI) helps illuminate the human vision system. Previous approaches primarily employ subject-specific models, sensitive to training sample size. We propose shallow subject-specific adapters to map cross-subject fMRI data into unified representations. During training, we leverage both visual and textual supervision for multi-modal brain decoding.
  arXiv  Detail & Related papers  (2024-03-11T01:18:49Z)
• Simple and Effective Transfer Learning for Neuro-Symbolic Integration [50.592338727912946]
  A potential solution to this issue is Neuro-Symbolic Integration (NeSy), where neural approaches are combined with symbolic reasoning. Most of these methods exploit a neural network to map perceptions to symbols and a logical reasoner to predict the output of the downstream task. They suffer from several issues, including slow convergence, learning difficulties with complex perception tasks, and convergence to local minima. This paper proposes a simple yet effective method to ameliorate these problems.
  arXiv  Detail & Related papers  (2024-02-21T15:51:01Z)
• Generalizable Neural Fields as Partially Observed Neural Processes [16.202109517569145]
  We propose a new paradigm that views the large-scale training of neural representations as a part of a partially-observed neural process framework. We demonstrate that this approach outperforms both state-of-the-art gradient-based meta-learning approaches and hypernetwork approaches.
  arXiv  Detail & Related papers  (2023-09-13T01:22:16Z)
• Versatile Neural Processes for Learning Implicit Neural Representations [57.090658265140384]
  We propose Versatile Neural Processes (VNP), which largely increases the capability of approximating functions. Specifically, we introduce a bottleneck encoder that produces fewer and informative context tokens, relieving the high computational cost. We demonstrate the effectiveness of the proposed VNP on a variety of tasks involving 1D, 2D and 3D signals.
  arXiv  Detail & Related papers  (2023-01-21T04:08:46Z)
• Towards Theoretically Inspired Neural Initialization Optimization [66.04735385415427]
  We propose a differentiable quantity, named GradCosine, with theoretical insights to evaluate the initial state of a neural network. We show that both the training and test performance of a network can be improved by maximizing GradCosine under norm constraint. Generalized from the sample-wise analysis into the real batch setting, NIO is able to automatically look for a better initialization with negligible cost.
  arXiv  Detail & Related papers  (2022-10-12T06:49:16Z)
• Neural Maximum A Posteriori Estimation on Unpaired Data for Motion Deblurring [87.97330195531029]
  We propose a Neural Maximum A Posteriori (NeurMAP) estimation framework for training neural networks to recover blind motion information and sharp content from unpaired data. The proposed NeurMAP is an approach to existing deblurring neural networks, and is the first framework that enables training image deblurring networks on unpaired datasets.
  arXiv  Detail & Related papers  (2022-04-26T08:09:47Z)
• Towards Lightweight Controllable Audio Synthesis with Conditional Implicit Neural Representations [10.484851004093919]
  Implicit neural representations (INRs) are neural networks used to approximate low-dimensional functions. In this work we shed light on the potential of Conditional Implicit Neural Representations (CINRs) as lightweight backbones in generative frameworks for audio synthesis.
  arXiv  Detail & Related papers  (2021-11-14T13:36:18Z)
• Neural Knitworks: Patched Neural Implicit Representation Networks [1.0470286407954037]
  We propose Knitwork, an architecture for neural implicit representation learning of natural images that achieves image synthesis. To the best of our knowledge, this is the first implementation of a coordinate-based patch tailored for synthesis tasks such as image inpainting, super-resolution, and denoising. The results show that modeling natural images using patches, rather than pixels, produces results of higher fidelity.
  arXiv  Detail & Related papers  (2021-09-29T13:10:46Z)
• Modeling from Features: a Mean-field Framework for Over-parameterized Deep Neural Networks [54.27962244835622]
  This paper proposes a new mean-field framework for over- parameterized deep neural networks (DNNs) In this framework, a DNN is represented by probability measures and functions over its features in the continuous limit. We illustrate the framework via the standard DNN and the Residual Network (Res-Net) architectures.
  arXiv  Detail & Related papers  (2020-07-03T01:37:16Z)
• Attend and Decode: 4D fMRI Task State Decoding Using Attention Models [2.6954666679827137]
  We present a novel architecture called Brain Attend and Decode (BAnD) BAnD uses residual convolutional neural networks for spatial feature extraction and self-attention mechanisms temporal modeling. We achieve significant performance gain compared to previous works on a 7-task benchmark from the Human Connectome Project-Young Adult dataset.
  arXiv  Detail & Related papers  (2020-04-10T21:29:34Z)

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.