Building Brain Tumor Segmentation Networks with User-Assisted Filter Estimation and Selection

• URL: http://arxiv.org/abs/2403.12748v1
• Date: Tue, 19 Mar 2024 14:11:26 GMT
• Title: Building Brain Tumor Segmentation Networks with User-Assisted Filter Estimation and Selection
• Authors: Matheus A. Cerqueira, Flávia Sprenger, Bernardo C. A. Teixeira, Alexandre X. Falcão,
• Abstract summary: We present Multi-Step (MS) FLIM, a user-assisted approach to estimating and selecting the most relevant filters from multiple FLIM executions. MS-FLIM is used only for the first convolutional layer, and the results already indicate improvement over FLIM. We build a simple U-shaped encoder-decoder network, named sU-Net, for glioblastoma segmentation using T1Gd and FLAIR MRI scans.
• Score: 42.62139206176152
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Brain tumor image segmentation is a challenging research topic in which deep-learning models have presented the best results. However, the traditional way of training those models from many pre-annotated images leaves several unanswered questions. Hence methodologies, such as Feature Learning from Image Markers (FLIM), have involved an expert in the learning loop to reduce human effort in data annotation and build models sufficiently deep for a given problem. FLIM has been successfully used to create encoders, estimating the filters of all convolutional layers from patches centered at marker voxels. In this work, we present Multi-Step (MS) FLIM - a user-assisted approach to estimating and selecting the most relevant filters from multiple FLIM executions. MS-FLIM is used only for the first convolutional layer, and the results already indicate improvement over FLIM. For evaluation, we build a simple U-shaped encoder-decoder network, named sU-Net, for glioblastoma segmentation using T1Gd and FLAIR MRI scans, varying the encoder's training method, using FLIM, MS-FLIM, and backpropagation algorithm. Also, we compared these sU-Nets with two State-Of-The-Art (SOTA) deep-learning models using two datasets. The results show that the sU-Net based on MS-FLIM outperforms the other training methods and achieves effectiveness within the standard deviations of the SOTA models.

Related papers

• MERLOT: A Distilled LLM-based Mixture-of-Experts Framework for Scalable Encrypted Traffic Classification [19.476061046309052]
  We present a scalable mixture-of-expert (MoE) based refinement of distilled large language model optimized for encrypted traffic classification. Experiments on 10 datasets show superior or competitive performance over state-of-the-art models.
  arXiv  Detail & Related papers  (2024-11-20T03:01:41Z)
• Filter Pruning for Efficient CNNs via Knowledge-driven Differential Filter Sampler [103.97487121678276]
  Filter pruning simultaneously accelerates the computation and reduces the memory overhead of CNNs. We propose a novel Knowledge-driven Differential Filter Sampler(KDFS) with Masked Filter Modeling(MFM) framework for filter pruning.
  arXiv  Detail & Related papers  (2023-07-01T02:28:41Z)
• Strong Baseline and Bag of Tricks for COVID-19 Detection of CT Scans [2.696776905220987]
  Traditional deep learning frameworks encounter compatibility issues due to variations in slice numbers and resolutions in CT images. We propose a novel slice selection method for each CT dataset to address this limitation. In addition to the aforementioned methods, we explore various high-performance classification models, ultimately achieving promising results.
  arXiv  Detail & Related papers  (2023-03-15T09:52:28Z)
• An Adaptive Plug-and-Play Network for Few-Shot Learning [12.023266104119289]
  Few-shot learning requires a model to classify new samples after learning from only a few samples. Deep networks and complex metrics tend to induce overfitting, making it difficult to further improve the performance. We propose plug-and-play model-adaptive resizer (MAR) and adaptive similarity metric (ASM) without any other losses.
  arXiv  Detail & Related papers  (2023-02-18T13:25:04Z)
• Model-Agnostic Multitask Fine-tuning for Few-shot Vision-Language Transfer Learning [59.38343286807997]
  We propose Model-Agnostic Multitask Fine-tuning (MAMF) for vision-language models on unseen tasks. Compared with model-agnostic meta-learning (MAML), MAMF discards the bi-level optimization and uses only first-order gradients. We show that MAMF consistently outperforms the classical fine-tuning method for few-shot transfer learning on five benchmark datasets.
  arXiv  Detail & Related papers  (2022-03-09T17:26:53Z)
• 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)
• Online Multi-Object Tracking and Segmentation with GMPHD Filter and Mask-based Affinity Fusion [79.87371506464454]
  We propose a fully online multi-object tracking and segmentation (MOTS) method that uses instance segmentation results as an input. The proposed method is based on the Gaussian mixture probability hypothesis density (GMPHD) filter, a hierarchical data association (HDA), and a mask-based affinity fusion (MAF) model. In the experiments on the two popular MOTS datasets, the key modules show some improvements.
  arXiv  Detail & Related papers  (2020-08-31T21:06:22Z)
• MetricUNet: Synergistic Image- and Voxel-Level Learning for Precise CT Prostate Segmentation via Online Sampling [66.01558025094333]
  We propose a two-stage framework, with the first stage to quickly localize the prostate region and the second stage to precisely segment the prostate. We introduce a novel online metric learning module through voxel-wise sampling in the multi-task network. Our method can effectively learn more representative voxel-level features compared with the conventional learning methods with cross-entropy or Dice loss.
  arXiv  Detail & Related papers  (2020-05-15T10:37:02Z)

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.