Multimodal brain tumor classification

• URL: http://arxiv.org/abs/2009.01592v2
• Date: Tue, 6 Oct 2020 16:05:22 GMT
• Title: Multimodal brain tumor classification
• Authors: Marvin Lerousseau, Eric Deutsh, Nikos Paragios
• Abstract summary: This work investigates a deep learning method combining whole slide images and magnetic resonance images to classify tumors. In particular, our solution comprises a powerful, generic and modular architecture for whole slide image classification. Experiments are prospectively conducted on the 2020 Computational Precision Medicine challenge.
• Score: 4.984601297028256
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cancer is a complex disease that provides various types of information depending on the scale of observation. While most tumor diagnostics are performed by observing histopathological slides, radiology images should yield additional knowledge towards the efficacy of cancer diagnostics. This work investigates a deep learning method combining whole slide images and magnetic resonance images to classify tumors. In particular, our solution comprises a powerful, generic and modular architecture for whole slide image classification. Experiments are prospectively conducted on the 2020 Computational Precision Medicine challenge, in a 3-classes unbalanced classification task. We report cross-validation (resp. validation) balanced-accuracy, kappa and f1 of 0.913, 0.897 and 0.951 (resp. 0.91, 0.90 and 0.94). For research purposes, including reproducibility and direct performance comparisons, our finale submitted models are usable off-the-shelf in a Docker image available at https://hub.docker.com/repository/docker/marvinler/cpm_2020_marvinler.

Related papers

• Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors [0.0]
  This work evaluates the performance of Vision Transformers architectures, including Swin Transformer and MaxViT, in several datasets. We used three training sets of images with brain, lung, and kidney tumors. Swin Transformer provided high accuracy, achieving up to 99.9% for kidney tumor classification and 99.3% accuracy in a combined dataset.
  arXiv  Detail & Related papers  (2025-02-08T10:35:51Z)
• Diverse Image Generation with Diffusion Models and Cross Class Label Learning for Polyp Classification [4.747649393635696]
  We develop a novel model, PathoPolyp-Diff, that generates text-controlled synthetic images with diverse characteristics. We introduce cross-class label learning to make the model learn features from other classes, reducing the burdensome task of data annotation.
  arXiv  Detail & Related papers  (2025-02-08T04:26:20Z)
• Brain Tumor Classification on MRI in Light of Molecular Markers [61.77272414423481]
  Co-deletion of the 1p/19q gene is associated with clinical outcomes in low-grade gliomas. This study aims to utilize a specially MRI-based convolutional neural network for brain cancer detection.
  arXiv  Detail & Related papers  (2024-09-29T07:04:26Z)
• Segmentation of the veterinary cytological images for fast neoplastic tumors diagnosis [0.14519981310202407]
  This paper shows the machine learning system which performs instance segmentation of cytological images in veterinary medicine. The deep learning models employed in the system achieve a high score of average precision and recall metrics.
  arXiv  Detail & Related papers  (2023-05-07T17:02:58Z)
• Automated SSIM Regression for Detection and Quantification of Motion Artefacts in Brain MR Images [54.739076152240024]
  Motion artefacts in magnetic resonance brain images are a crucial issue. The assessment of MR image quality is fundamental before proceeding with the clinical diagnosis. An automated image quality assessment based on the structural similarity index (SSIM) regression has been proposed here.
  arXiv  Detail & Related papers  (2022-06-14T10:16:54Z)
• EMT-NET: Efficient multitask network for computer-aided diagnosis of breast cancer [58.720142291102135]
  We propose an efficient and light-weighted learning architecture to classify and segment breast tumors simultaneously. We incorporate a segmentation task into a tumor classification network, which makes the backbone network learn representations focused on tumor regions. The accuracy, sensitivity, and specificity of tumor classification is 88.6%, 94.1%, and 85.3%, respectively.
  arXiv  Detail & Related papers  (2022-01-13T05:24:40Z)
• 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)
• Malignancy Prediction and Lesion Identification from Clinical Dermatological Images [65.1629311281062]
  We consider machine-learning-based malignancy prediction and lesion identification from clinical dermatological images. We first identify all lesions present in the image regardless of sub-type or likelihood of malignancy, then it estimates their likelihood of malignancy, and through aggregation, it also generates an image-level likelihood of malignancy.
  arXiv  Detail & Related papers  (2021-04-02T20:52:05Z)
• A Deep Learning Study on Osteosarcoma Detection from Histological Images [6.341765152919201]
  The most common type of primary malignant bone tumor is osteosarcoma. CNNs can significantly decrease surgeon's workload and make a better prognosis of patient conditions. CNNs need to be trained on a large amount of data in order to achieve a more trustworthy performance.
  arXiv  Detail & Related papers  (2020-11-02T18:16:17Z)
• 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)
• Classification of COVID-19 in CT Scans using Multi-Source Transfer Learning [91.3755431537592]
  We propose the use of Multi-Source Transfer Learning to improve upon traditional Transfer Learning for the classification of COVID-19 from CT scans. With our multi-source fine-tuning approach, our models outperformed baseline models fine-tuned with ImageNet. Our best performing model was able to achieve an accuracy of 0.893 and a Recall score of 0.897, outperforming its baseline Recall score by 9.3%.
  arXiv  Detail & Related papers  (2020-09-22T11:53:06Z)

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.