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medical-9

Colorectal Gland Segmentation:

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in colorectal-gland-segmentation-9

Trend

Dataset

Best Model

Actions

CRAG

STARE

Libraries

Use these libraries to find colorectal-gland-segmentation-9 models and implementations

Datasets

Kvasir-SEG

STARE

Kvasir

Subtasks

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Most implemented papers

U-Net: Convolutional Networks for Biomedical Image Segmentation

O. Ronneberger, T. Brox, P. Fischer•Sun May 17 2015

It is shown that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.

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Introduction Benchmarks Datasets Subtasks Libraries Papers

Paper Graph

Rotation Equivariant Vector Field Networks

D. Tuia, N. Komodakis, Diego Marcos, M. Volpi•Wed Dec 28 2016

The Rotation Equivariant Vector Field Networks (RotEqNet), a Convolutional Neural Network architecture encoding rotation equivariance, invariance and covariance, is proposed and a modified convolution operator relying on this representation to obtain deep architectures is developed.

284 0

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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.

96 0

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Pseudo-Label Guided Contrastive Learning for Semi-Supervised Medical Image Segmentation

Hritam Basak•Wed May 31 2023

Although recent works in semi-supervised learning (SemiSL) have accomplished significant success in natural image segmentation, the task of learning discriminative representations from limited annotations has been an open problem in medical images. Contrastive Learning (CL) frameworks use the notion of similarity measure which is useful for classification problems, however, they fail to transfer these quality representations for accurate pixel-level segmentation. To this end, we propose a novel semi-supervised patch-based CL framework for medical image segmentation without using any explicit pretext task. We harness the power of both CL and SemiSL, where the pseudo-labels generated from SemiSL aid CL by providing additional guidance, whereas discriminative class information learned in CL leads to accurate multi-class segmentation. Additionally, we formulate a novel loss that synergistically encourages inter-class separability and intraclass compactness among the learned representations. A new inter-patch semantic disparity mapping using average patch entropy is employed for a guided sampling of positives and negatives in the proposed CL framework. Experimental analysis on three publicly available datasets of multiple modalities reveals the superiority of our proposed method as compared to the state-of-the-art methods. Code is available at: GitHub.

121 0

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Group Equivariant Convolutional Networks

Taco Cohen, M. Welling•Tue Feb 23 2016

Group equivariant Convolutional Neural Networks (G-CNNs), a natural generalization of convolutional neural networks that reduces sample complexity by exploiting symmetries and achieves state of the art results on CI- FAR10 and rotated MNIST.

2192 0

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Machine learning approach for segmenting glands in colon histology images using local intensity and texture features

S. Chatterjee, Rupali Khatun•Fri Nov 30 2018

An algorithm for an automatic segmentation of glands in colon histology using local intensity and texture features using a multilevel random forest technique in a hierarchical way is proposed.

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

M. Veta, J. Pluim, Maxime W. Lafarge, E. Bekkers, R. Duits•Wed Feb 19 2020

A framework to encode the geometric structure of the special Euclidean motion group SE(2) in convolutional networks to yield translation and rotation equivariance via the introduction of SE( 2)-group convolution layers is proposed.

85 0

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Colorectal Gland Segmentation: | State-of-the-Art