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Greatest papers with code

Unsupervised Deep Learning for Bayesian Brain MRI Segmentation

25 Apr 2019voxelmorph/voxelmorph

To develop a deep learning-based segmentation model for a new image dataset (e. g., of different contrast), one usually needs to create a new labeled training dataset, which can be prohibitively expensive, or rely on suboptimal ad hoc adaptation or augmentation approaches.

BRAIN IMAGE SEGMENTATION BRAIN SEGMENTATION FEW-SHOT SEMANTIC SEGMENTATION IMAGE REGISTRATION ZERO SHOT SEGMENTATION

HyperDense-Net: A hyper-densely connected CNN for multi-modal image segmentation

9 Apr 2018black0017/MedicalZooPytorch

Therefore, the proposed network has total freedom to learn more complex combinations between the modalities, within and in-between all the levels of abstraction, which increases significantly the learning representation.

BRAIN SEGMENTATION IMAGE CLASSIFICATION MULTI-MODAL IMAGE SEGMENTATION REPRESENTATION LEARNING SEMANTIC SEGMENTATION

Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning

29 Oct 2018arnab39/FewShot_GAN-Unet3D

In addition, our work presents a comprehensive analysis of different GAN architectures for semi-supervised segmentation, showing recent techniques like feature matching to yield a higher performance than conventional adversarial training approaches.

3D MEDICAL IMAGING SEGMENTATION BRAIN IMAGE SEGMENTATION BRAIN SEGMENTATION FEW-SHOT SEMANTIC SEGMENTATION SEMI-SUPERVISED SEMANTIC SEGMENTATION

A Contrast-Adaptive Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis

11 May 2020freesurfer/freesurfer

Here we present a method for the simultaneous segmentation of white matter lesions and normal-appearing neuroanatomical structures from multi-contrast brain MRI scans of multiple sclerosis patients.

3D MEDICAL IMAGING SEGMENTATION BRAIN IMAGE SEGMENTATION BRAIN LESION SEGMENTATION FROM MRI BRAIN SEGMENTATION LESION SEGMENTATION

Recalibrating 3D ConvNets with Project & Excite

25 Feb 2020ai-med/squeeze_and_excitation

Fully Convolutional Neural Networks (F-CNNs) achieve state-of-the-art performance for segmentation tasks in computer vision and medical imaging.

BRAIN SEGMENTATION

`Project & Excite' Modules for Segmentation of Volumetric Medical Scans

11 Jun 2019abhi4ssj/squeeze_and_excitation

Fully Convolutional Neural Networks (F-CNNs) achieve state-of-the-art performance for image segmentation in medical imaging.

BRAIN SEGMENTATION SEMANTIC SEGMENTATION

Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks

7 Mar 2018abhi4ssj/squeeze_and_excitation

Fully convolutional neural networks (F-CNNs) have set the state-of-the-art in image segmentation for a plethora of applications.

BRAIN SEGMENTATION IMAGE CLASSIFICATION SEMANTIC SEGMENTATION