Frequency selection for the diagnostic characterization of human brain tumours

• URL: http://arxiv.org/abs/2503.08756v1
• Date: Tue, 11 Mar 2025 15:46:27 GMT
• Title: Frequency selection for the diagnostic characterization of human brain tumours
• Authors: Carlos Arizmendi, Alfredo Vellido, Enrique Romero,
• Abstract summary: The diagnosis of brain tumours is an extremely sensitive and complex clinical task.<n>The latter provides plenty of metabolic information about the tumour tissue, but its high dimensionality makes resorting to pattern recognition techniques advisable.
• Score: 0.08192907805418582
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The diagnosis of brain tumours is an extremely sensitive and complex clinical task that must rely upon information gathered through non-invasive techniques. One such technique is magnetic resonance, in the modalities of imaging or spectroscopy. The latter provides plenty of metabolic information about the tumour tissue, but its high dimensionality makes resorting to pattern recognition techniques advisable. In this brief paper, an international database of brain tumours is analyzed resorting to an ad hoc spectral frequency selection procedure combined with nonlinear classification.

Related papers

• A CNN Approach to Automated Detection and Classification of Brain Tumors [0.0]
  This research aims to categorize healthy brain tissue and brain tumors by analyzing the provided MRI data.<n>The dataset utilized for the models creation is a publicly accessible and validated Brain Tumour Classification (MRI) database, comprising 3,264 brain MRI scans.
  arXiv  Detail & Related papers  (2025-02-13T19:33:26Z)
• Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations [0.0]
  Surgery for brain cancer is a major problem in neurosurgery. The identification of the tumor boundaries during surgery is challenging. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images.
  arXiv  Detail & Related papers  (2024-02-11T12:58:42Z)
• UniBrain: Universal Brain MRI Diagnosis with Hierarchical Knowledge-enhanced Pre-training [66.16134293168535]
  We propose a hierarchical knowledge-enhanced pre-training framework for the universal brain MRI diagnosis, termed as UniBrain. Specifically, UniBrain leverages a large-scale dataset of 24,770 imaging-report pairs from routine diagnostics.
  arXiv  Detail & Related papers  (2023-09-13T09:22:49Z)
• Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging [59.79875531898648]
  DeepGlioma is an artificial-intelligence-based diagnostic screening system. DeepGlioma can predict the molecular alterations used by the World Health Organization to define the adult-type diffuse glioma taxonomy.
  arXiv  Detail & Related papers  (2023-03-23T18:50:18Z)
• Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI [55.78588835407174]
  We propose a method that reformulates the generation task of diffusion models as a patch-based estimation of healthy brain anatomy. We evaluate our approach on data of tumors and multiple sclerosis lesions and demonstrate a relative improvement of 25.1% compared to existing baselines.
  arXiv  Detail & Related papers  (2023-03-07T09:40:22Z)
• Triplet Contrastive Learning for Brain Tumor Classification [99.07846518148494]
  We present a novel approach of directly learning deep embeddings for brain tumor types, which can be used for downstream tasks such as classification. We evaluate our method on an extensive brain tumor dataset which consists of 27 different tumor classes, out of which 13 are defined as rare.
  arXiv  Detail & Related papers  (2021-08-08T11:26:34Z)
• MAG-Net: Mutli-task attention guided network for brain tumor segmentation and classification [0.9176056742068814]
  This paper proposes multi-task attention guided encoder-decoder network (MAG-Net) to classify and segment the brain tumor regions using MRI images. The model achieved promising results as compared to existing state-of-the-art models.
  arXiv  Detail & Related papers  (2021-07-26T16:51:00Z)
• Learned super resolution ultrasound for improved breast lesion characterization [52.77024349608834]
  Super resolution ultrasound localization microscopy enables imaging of the microvasculature at the capillary level. In this work we use a deep neural network architecture that makes effective use of signal structure to address these challenges. By leveraging our trained network, the microvasculature structure is recovered in a short time, without prior PSF knowledge, and without requiring separability of the UCAs.
  arXiv  Detail & Related papers  (2021-07-12T09:04:20Z)
• Experimenting with Knowledge Distillation techniques for performing Brain Tumor Segmentation [0.0]
  Multi-modal magnetic resonance imaging (MRI) is a crucial method for analyzing the human brain. With varying degrees of severity and detection, properly diagnosing gliomas is one of the most daunting and significant analysis tasks in modern-day medicine. Our primary focus is on working with different approaches to perform the segmentation of brain tumors in multimodal MRI scans.
  arXiv  Detail & Related papers  (2021-05-24T18:17:01Z)
• QuickTumorNet: Fast Automatic Multi-Class Segmentation of Brain Tumors [0.0]
  Manual segmentation of brain tumors from 3D MRI volumes is a time-consuming task. Our model, QuickTumorNet, demonstrated fast, reliable, and accurate brain tumor segmentation.
  arXiv  Detail & Related papers  (2020-12-22T23:16:43Z)
• Spectral-Spatial Recurrent-Convolutional Networks for In-Vivo Hyperspectral Tumor Type Classification [49.32653090178743]
  We demonstrate the feasibility of in-vivo tumor type classification using hyperspectral imaging and deep learning. Our best model achieves an AUC of 76.3%, significantly outperforming previous conventional and deep learning methods.
  arXiv  Detail & Related papers  (2020-07-02T12:00:53Z)

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.