Tumour Classification
8 papers with code • 0 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Tumour Classification
Radio-Freqency Ultrasound volume dataset for pre-clinical liver tumors
Most implemented papers
Integrated Multi-omics Analysis Using Variational Autoencoders: Application to Pan-cancer Classification
The training procedure of OmiVAE is comprised of an unsupervised phase without the classifier and a supervised phase with the classifier.
Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images
Histology images are inherently symmetric under rotation, where each orientation is equally as likely to appear.
XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data
To the best of our knowledge, XOmiVAE is one of the first activation level-based interpretable deep learning models explaining novel clusters generated by VAE.
Classification of Brain Tumours in MR Images using Deep Spatiospatial Models
farazahmeds/Classification-of-brain-tumor-using-Spatiotemporal-models
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Finally, Pre-trained ResNet Mixed Convolution was observed to be the best model in these experiments, achieving a macro F1-score of 0. 93 and a test accuracy of 96. 98\%, while at the same time being the model with the least computational cost.
Weakly-supervised segmentation using inherently-explainable classification models and their application to brain tumour classification
The models are trained by using the input image and only the classification labels as ground-truth in a supervised fashion - without using any information about the location of the region of interest (i. e. the segmentation labels), making the segmentation training of the models weakly-supervised through classification labels.
Complex Network for Complex Problems: A comparative study of CNN and Complex-valued CNN
Although some comparisons of CNNs and CV-CNNs for different tasks have been performed in the past, a large-scale investigation comparing different models operating on different tasks has not been conducted.
Genetic Analysis of Prostate Cancer with Computer Science Methods
Metastatic prostate cancer is one of the most common cancers in men.
Deep Learning Approaches to Osteosarcoma Diagnosis and Classification: A Comparative Methodological Approach
ivezakis/dl-osteo-2023
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Cancers 2023
Methods: This study used publicly available images of osteosarcoma cross-sections to analyze and compare the performance of state-of-the-art deep neural networks for histopathological evaluation of osteosarcomas.
