Breast Tumour Classification

9 papers with code • 1 benchmarks • 1 datasets

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Greatest papers with code

Deep Residual Learning for Image Recognition

tensorflow/models CVPR 2016

Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

Breast Tumour Classification Domain Generalization +8

Densely Connected Convolutional Networks

pytorch/vision 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.

Breast Tumour Classification Crowd Counting +4

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.

Breast Tumour Classification

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.

Breast Tumour Classification Colorectal Gland Segmentation: +2

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.

Breast Tumour Classification Colorectal Gland Segmentation: +3

Rotation equivariant vector field networks


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

Breast Tumour Classification Colorectal Gland Segmentation: +4

Self-supervised driven consistency training for annotation efficient histopathology image analysis

srinidhiPY/SSL_CR_Histo 7 Feb 2021

In this work, we overcome this challenge by leveraging both task-agnostic and task-specific unlabeled data based on two novel strategies: i) a self-supervised pretext task that harnesses the underlying multi-resolution contextual cues in histology whole-slide images to learn a powerful supervisory signal for unsupervised representation learning; ii) a new teacher-student semi-supervised consistency paradigm that learns to effectively transfer the pretrained representations to downstream tasks based on prediction consistency with the task-specific un-labeled data.

Breast Tumour Classification Classification Of Breast Cancer Histology Images +2

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

MIMBCD-UI/meta 7 Apr 2020

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

3D Medical Imaging Segmentation Automatic Machine Learning Model Selection +8