medical-7

Tumour Classification

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in tumour-classification-7

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Libraries

i

Use these libraries to find tumour-classification-7 models and implementations

Datasets

Radio-Freqency Ultrasound volume dataset for pre-clinical liver tumors

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Most implemented papers

Integrated Multi-omics Analysis Using Variational Autoencoders: Application to Pan-cancer Classification

Jingqing Zhang, Yike Guo, Xiaoyu Zhang, Kai Sun, Xian Yang, Chengliang Dai•Fri Aug 16 2019

The integrated model learned from multi-omics datasets outperformed those using only one type of omics data, which indicates that the complementary information from different omics datatypes provides useful insights for biomedical tasks like cancer classification.

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Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images

N. Rajpoot, S. Graham, David B. A. Epstein•Sun Apr 05 2020

Dense Steerable Filter CNNs (DSF-CNNs) that use group convolutions with multiple rotated copies of each filter in a densely connected framework are proposed that achieve state-of-the-art performance, with significantly fewer parameters, when applied to three different tasks in the area of computational pathology.

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Classification of brain tumours in MR images using deep spatiospatial models

A. Nürnberger, S. Chatterjee, O. Speck, F. Nizamani•Thu May 27 2021

Two spatiotemporal models, ResNet (2+1)D and ResNet Mixed Convolution, are used to classify different types of brain tumours, and it was observed that both these models performed superior to the pure 3D convolutional model, Res net18.

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XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data

Yike Guo, Eloise Withnell, Xiaoyu Zhang, Kai Sun•Tue May 25 2021

In experiments, XOmiVAE explanations of deep learning-based cancer classification and clustering aligned with current domain knowledge including biological annotation and academic literature, which shows great potential for novel biomedical knowledge discovery from deep learning models.

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Weakly-supervised segmentation using inherently-explainable classification models and their application to brain tumour classification

A. Nürnberger, S. Chatterjee, O. Speck, Hadya Yassin, Florian Dubost•Thu Jun 09 2022

The proposed framework for three inherently explainable classifiers (GP-UNet, GP-ShuffleUNet, and GP-ReconResNet) successfully combines high diagnostic accuracy with essential transparency, offering a promising direction for trustworthy clinical decision support.

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Complex Network for Complex Problems: A comparative study of CNN and Complex-valued CNN

A. Nürnberger, S. Chatterjee, O. Speck, Pavan Tummala•Sun Dec 04 2022

A comparative study of CNN, CNNx2 (CNN with double the number of trainable parameters as the CNN), and CV-CNN, using seven models for two different tasks - brain tumour classification and segmentation in brain MRIs revealed that the CV- CNN models outperformed the CNN and CNNx3 models.

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Genetic Analysis of Prostate Cancer with Computer Science Methods

Yuxuan Li, Shifang Zhou•Mon Mar 27 2023

13 of the 14,379 genes were selected as the most metastatic prostate cancer related genes, achieving approximately 92% accuracy under cross-validation, and preliminary insights into the co-expression patterns of genes in gene co- expression networks are provided.

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Deep Learning Approaches to Osteosarcoma Diagnosis and Classification: A Comparative Methodological Approach

Ioannis A Vezakis, G. Lambrou, G. Matsopoulos•Fri Mar 31 2023

The present study highlights the importance of careful selection of network and input image size, and indicates that a larger number of parameters is not always better, and the best results can be achieved on smaller and more efficient networks.

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Brain tumour classification using BoF-SURF with filter-based feature selection methods

Zhana Fidakar Mohammed, Diyari Jalal Mussa•Thu Jan 18 2024

The results of the authors' experiments strongly support the effectiveness of the proposed method, achieving an impressive accuracy rate of 98.7% and outcomes clearly outperform existing approaches.

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