poining

poining's Stars

• Rayicer/TransFuse

  This repo holds the code of TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation

  Language:Python18031

• deaspo/Unet_MedicalImagingSegmentation

  A U-Net deep learning model for Segmentation of CT Images

  Language:Python52

• xmu-xiaoma666/External-Attention-pytorch

  🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐

  Language:Python11.4k1.9k

• AlexTS1980/COVID-LSTM-Attention

  COVID-19 Classification + Segmentation Using One Shot Model with LSTM + Attention Mechanism

  Language:Python91

• xiaoxuegao499/LA-DNN-for-COVID-19-diagnosis

  Online COVID-19 diagnosis with chest CT images: Lesion-attention deep neural networks

  Language:Python6418

• jalexnoel/ADID-UNET

  A new segment depth network for COVID-19 lung CT scans, Attention Gate-Dense Network- Improved Dilation Convolution-UNET (ADID-UNET)

  61

• HuHaigen/COVID-19-Lung-Infection-Segmentation

  Due to the irregular shapes,various sizes and indistinguishable boundaries between the normal and infected tissues, it is still a challenging task to accurately segment the infected lesions of COVID-19 on CT images. In this paper, a novel segmentation scheme is proposed for the infections of COVID-19 by enhancing supervised information and fusing multi-scale feature maps of different levels based on the encoder-decoder architecture. To this end, a deep collaborative supervision (Co-supervision) scheme is proposed to guide the network learning the features of edges and semantics. More specifically, an Edge Supervised Module (ESM) is firstly designed to highlight low-level boundary features by incorporating the edge supervised information into the initial stage of down-sampling. Meanwhile, an Auxiliary Semantic Supervised Module (ASSM) is proposed to strengthen high-level semantic information by integrating mask supervised information into the later stage. Then an Attention Fusion Module (AFM) is developed to fuse multiple scale feature maps of different levels by using an attention mechanism to reduce the semantic gaps between high-level and low-level feature maps. Finally, the effectiveness of the proposed scheme is demonstrated on four various COVID-19 CT datasets. The results show that the proposed three modules are all promising. Based on the baseline (ResUnet), using ESM, ASSM, or AFM alone can respectively increase Dice metric by 1.12%, 1.95%,1.63% in our dataset, while the integration by incorporating three models together can rise 3.97%. Compared with the existing approaches in various datasets, the proposed method can obtain better segmentation performance in some main metrics, and can achieve the best generalization and comprehensive performance.

  Language:Python141

• sumanyumuku98/CovidAid_V2

  COVID-19 Detection Using CXR and Attention Guided CNN

  Language:Python53

• Sreehari-S/Recurrent-Attention-model-RAM

  Recurrent Attention Models - Pytorch implementation for medical image classification

  Language:Python4

• WZMIAOMIAO/deep-learning-for-image-processing

  deep learning for image processing including classification and object-detection etc.

  Language:Python22.8k8k

• MoleImg/Attention_UNet

  Raw implementation of attention gated U-Net by Keras

  Language:Python14543

• bigmb/Unet-Segmentation-Pytorch-Nest-of-Unets

  Implementation of different kinds of Unet Models for Image Segmentation - Unet , RCNN-Unet, Attention Unet, RCNN-Attention Unet, Nested Unet

  Language:Python1.9k345

• enpeizhao/CVprojects

  computer vision projects | 计算机视觉相关好玩的AI项目（Python、C++、embedded system）

  Language:Jupyter Notebook2.2k668

• YiYuanIntelligent/3DFasterRCNN_LungNoduleDetector

  Language:Python418170

• alfharan/COVID-19-Detection-Device

  COVID-19 detection device based on the Convolutional Neural Network (CNN) using X-ray images

  Language:Python51

• dongwuuu/COVID-19-Classification

  COVID-19 NCP CNN classification medical image

  Language:Python12

• aniruddh-1/COVID19_Pneumonia_detection

  Detects Covid-19 Pneumonia signs from CT Scan Images by a CNN Model

  Language:Python7

• flavioragni/CT_covid_detection_pyTorch

  CNN for COVID-19 detection in CT images using pyTorch

  Language:Jupyter Notebook1

• Asraf047/COVID19-CNN-LSTM

  A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images

  Language:Python92

• AlexTS1980/COVID-CT-Mask-Net

  Segmentation and Classification models for COVID CT scans (COVID, pneumonia, normal) based on Mask R-CNN.

  Language:Python2715

• hariharan98m/covid-ct-seg

  COVID-19 infection segmentation from CT scan using Contour-aware Attention CNN

  Language:Python1

• xmuyzz/3D-CNN-PyTorch

  PyTorch implementation for 3D CNN models for medical image data (1 channel gray scale images).

  Language:Python14627