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Greatest papers with code

Densely Connected Convolutional Networks

CVPR 2017 pytorch/vision

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 IMAGE CLASSIFICATION OBJECT RECOGNITION PEDESTRIAN ATTRIBUTE RECOGNITION PERSON RE-IDENTIFICATION

Rotation Equivariant CNNs for Digital Pathology

8 Jun 2018basveeling/pcam

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

24 Feb 2016adambielski/pytorch-gconv-experiments

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: MULTI-TISSUE NUCLEUS SEGMENTATION ROTATED MNIST

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

20 Feb 2020tueimage/se2cnn

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: DATA AUGMENTATION MITOSIS DETECTION MULTI-TISSUE NUCLEUS SEGMENTATION

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

7 Feb 2021srinidhiPY/SSL_CR_Histo

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 HISTOPATHOLOGICAL IMAGE CLASSIFICATION SELF-SUPERVISED LEARNING