Triplet Contrastive Learning for Brain Tumor Classification
- URL: http://arxiv.org/abs/2108.03611v1
- Date: Sun, 8 Aug 2021 11:26:34 GMT
- Title: Triplet Contrastive Learning for Brain Tumor Classification
- Authors: Tian Yu Liu and Jiashi Feng
- Abstract summary: 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.
- Score: 99.07846518148494
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Brain tumor is a common and fatal form of cancer which affects both adults
and children. The classification of brain tumors into different types is hence
a crucial task, as it greatly influences the treatment that physicians will
prescribe. In light of this, medical imaging techniques, especially those
applying deep convolutional networks followed by a classification layer, have
been developed to make possible computer-aided classification of brain tumor
types. In this paper, we present a novel approach of directly learning deep
embeddings for brain tumor types, which can be used for downstream tasks such
as classification. Along with using triplet loss variants, our approach applies
contrastive learning to performing unsupervised pre-training, combined with a
rare-case data augmentation module to effectively ameliorate the lack of data
problem in the brain tumor imaging analysis domain. 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. With a common encoder during all the
experiments, we compare our approach with a baseline classification-layer based
model, and the results well prove the effectiveness of our approach across all
measured metrics.
Related papers
- 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) - Automated Ensemble-Based Segmentation of Adult Brain Tumors: A Novel
Approach Using the BraTS AFRICA Challenge Data [0.0]
We introduce an ensemble method that comprises eleven unique variations based on three core architectures.
Our findings reveal that the ensemble approach, combining different architectures, outperforms single models.
These results underline the potential of tailored deep learning techniques in precisely segmenting brain tumors.
arXiv Detail & Related papers (2023-08-14T15:34:22Z) - Prediction of brain tumor recurrence location based on multi-modal
fusion and nonlinear correlation learning [55.789874096142285]
We present a deep learning-based brain tumor recurrence location prediction network.
We first train a multi-modal brain tumor segmentation network on the public dataset BraTS 2021.
Then, the pre-trained encoder is transferred to our private dataset for extracting the rich semantic features.
Two decoders are constructed to jointly segment the present brain tumor and predict its future tumor recurrence location.
arXiv Detail & Related papers (2023-04-11T02:45:38Z) - 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) - Detection and Classification of Brain tumors Using Deep Convolutional
Neural Networks [0.0]
Tumour in the brain is fatal as it may be cancerous.
There are different sizes and locations of brain tumors which makes it difficult to understand their nature.
This paper is to differentiate between normal and abnormal pixels and also classify them with better accuracy.
arXiv Detail & Related papers (2022-08-28T18:24:22Z) - Multi-Classification of Brain Tumor Images Using Transfer Learning Based
Deep Neural Network [0.5893124686141781]
This paper focuses on elevating the classification accuracy of brain tumor images with transfer learning based deep neural network.
The proposed model acquires an effective performance with an overall accuracy of 96.25% which is much improved than some existing multi-classification methods.
arXiv Detail & Related papers (2022-06-17T04:30:40Z) - Cross-Modality Deep Feature Learning for Brain Tumor Segmentation [158.8192041981564]
This paper proposes a novel cross-modality deep feature learning framework to segment brain tumors from the multi-modality MRI data.
The core idea is to mine rich patterns across the multi-modality data to make up for the insufficient data scale.
Comprehensive experiments are conducted on the BraTS benchmarks, which show that the proposed cross-modality deep feature learning framework can effectively improve the brain tumor segmentation performance.
arXiv Detail & Related papers (2022-01-07T07:46:01Z) - Medulloblastoma Tumor Classification using Deep Transfer Learning with
Multi-Scale EfficientNets [63.62764375279861]
We propose an end-to-end MB tumor classification and explore transfer learning with various input sizes and matching network dimensions.
Using a data set with 161 cases, we demonstrate that pre-trained EfficientNets with larger input resolutions lead to significant performance improvements.
arXiv Detail & Related papers (2021-09-10T13:07:11Z) - Brain Tumors Classification for MR images based on Attention Guided Deep
Learning Model [3.6328238032703806]
We analyze the existing technology and propose an attention guided deep convolution neural network (CNN) model.
Our method can achieve the average accuracy of 99.18% under ten-fold cross-validation for identifying the presence or absence of tumor.
It can assist doctors in achieving efficient clinical diagnosis of brain tumors.
arXiv Detail & Related papers (2021-04-06T07:25:52Z) - Brain Tumor Classification Using Medial Residual Encoder Layers [9.038707616951795]
Cancer is the second leading cause of death worldwide, responsible for over 9.5 million deaths in 2018 alone.
Brain tumors count for one out of every four cancer deaths.
We propose a system based on deep learning, containing encoder blocks. These blocks are fed with post-max-pooling features as residual learning.
Experimental evaluations of this model on a dataset consisting of 3064 MR images show 95.98% accuracy, which is better than previous studies on this database.
arXiv Detail & Related papers (2020-11-01T21:19:38Z)
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.