Breast Tumour Classification

8 papers with code • 1 benchmarks • 4 datasets

This task has no description! Would you like to contribute one?


Use these libraries to find Breast Tumour Classification models and implementations
2 papers
2 papers
See all 7 libraries.

Most implemented papers

Densely Connected Convolutional Networks

liuzhuang13/DenseNet CVPR 2017

Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output.

BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis

MIMBCD-UI/prototype-multi-modality 7 Apr 2020

This paper describes the field research, design and comparative deployment of a multimodal medical imaging user interface for breast screening.

Rotation Equivariant CNNs for Digital Pathology

basveeling/pcam 8 Jun 2018

We propose a new model for digital pathology segmentation, based on the observation that histopathology images are inherently symmetric under rotation and reflection.

Rotation equivariant vector field networks

di-marcos/RotEqNet ICCV 2017

In many computer vision tasks, we expect a particular behavior of the output with respect to rotations of the input image.

Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images

simongraham/dsf-cnn 6 Apr 2020

Histology images are inherently symmetric under rotation, where each orientation is equally as likely to appear.

Group Equivariant Convolutional Networks

adambielski/pytorch-gconv-experiments 24 Feb 2016

We introduce Group equivariant Convolutional Neural Networks (G-CNNs), a natural generalization of convolutional neural networks that reduces sample complexity by exploiting symmetries.

Roto-Translation Equivariant Convolutional Networks: Application to Histopathology Image Analysis

tueimage/se2cnn 20 Feb 2020

This study is focused on histopathology image analysis applications for which it is desirable that the arbitrary global orientation information of the imaged tissues is not captured by the machine learning models.

Meta-repository of screening mammography classifiers

nyukat/mammography_metarepository 10 Aug 2021

Artificial intelligence (AI) is showing promise in improving clinical diagnosis.