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Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation

20 Feb 2018LeeJunHyun/Image_Segmentation

In this paper, we propose a Recurrent Convolutional Neural Network (RCNN) based on U-Net as well as a Recurrent Residual Convolutional Neural Network (RRCNN) based on U-Net models, which are named RU-Net and R2U-Net respectively.

IMAGE CLASSIFICATION LESION SEGMENTATION LUNG NODULE SEGMENTATION RETINAL VESSEL SEGMENTATION SEMANTIC SEGMENTATION SKIN CANCER SEGMENTATION

Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis

19 Aug 2019MrGiovanni/ModelsGenesis

More importantly, learning a model from scratch simply in 3D may not necessarily yield performance better than transfer learning from ImageNet in 2D, but our Models Genesis consistently top any 2D approaches including fine-tuning the models pre-trained from ImageNet as well as fine-tuning the 2D versions of our Models Genesis, confirming the importance of 3D anatomical information and significance of our Models Genesis for 3D medical imaging.

BRAIN TUMOR SEGMENTATION LIVER SEGMENTATION LUNG NODULE DETECTION LUNG NODULE SEGMENTATION PULMONARY EMBOLISM DETECTION SELF-SUPERVISED LEARNING TRANSFER LEARNING

Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions

31 Aug 2019rezazad68/BCDU-Net

To strengthen feature propagation and encourage feature reuse, we use densely connected convolutions in the last convolutional layer of the encoding path.

 Ranked #1 on Lesion Segmentation on ISIC 2018 (F1-Score metric)

LESION SEGMENTATION LUNG NODULE SEGMENTATION SEMANTIC SEGMENTATION

Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restoration

14 Jul 2020JLiangLab/SemanticGenesis

To this end, we train deep models to learn semantically enriched visual representation by self-discovery, self-classification, and self-restoration of the anatomy underneath medical images, resulting in a semantics-enriched, general-purpose, pre-trained 3D model, named Semantic Genesis.

BRAIN TUMOR SEGMENTATION LIVER SEGMENTATION LUNG NODULE DETECTION LUNG NODULE SEGMENTATION REPRESENTATION LEARNING SELF-SUPERVISED LEARNING TRANSFER LEARNING

Level set image segmentation with velocity term learned from data with applications to lung nodule segmentation

8 Oct 2019notmatthancock/level-set-machine-learning

Approach: We introduce an extension of the standard level set image segmentation method where the velocity function is learned from data via machine learning regression methods, rather than a priori designed.

LUNG NODULE SEGMENTATION SEMANTIC SEGMENTATION

iW-Net: an automatic and minimalistic interactive lung nodule segmentation deep network

30 Nov 2018gmaresta/iW-Net

We propose iW-Net, a deep learning model that allows for both automatic and interactive segmentation of lung nodules in computed tomography images.

INTERACTIVE SEGMENTATION LUNG NODULE SEGMENTATION

U-Det: A Modified U-Net architecture with bidirectional feature network for lung nodule segmentation

20 Mar 2020NikV-JS/U-Det

Early diagnosis and analysis of lung cancer involve a precise and efficient lung nodule segmentation in computed tomography (CT) images.

COMPUTED TOMOGRAPHY (CT) LUNG NODULE SEGMENTATION