thithaotran Goto Github PK

keras-vision-transformer

The Tensorflow, Keras implementation of Swin-Transformer and Swin-UNET

label-efficient-brain-tumor-segmentation

learning-action-completeness-from-points

Official Pytorch Implementation of 'Learning Action Completeness from Points for Weakly-supervised Temporal Action Localization' (ICCV-21 Oral)

levelsets

Code for our draft "Numerical Exploration of Training Loss Level-Sets in Deep Neural Networks"

libfewshot

LibFewShot: A Comprehensive Library for Few-shot Learning.

lstm-unet

mamba-unet

Mamba-UNet Zoo

mamba_state_space_model_paper_list

[Mamba-Survey-2024] Paper list for State-Space-Model/Mamba and it's Applications

mctrans

medical-cross-modality-domain-adaptation

[IJCAI'18] Unsupervised Cross-Modality Domain Adaptation of ConvNets for Biomedical Image Segmentations with Adversarial Loss (code&data)

medical-transformer

Pytorch Code for "Medical Transformer: Gated Axial-Attention for Medical Image Segmentation"

medicaldataaugmentationtool

medicaldatalab-hackforgood2019

Aquí encontraremos los archivos del proyecto realizado por nuestro grupo para resolver el reto de Inteligencia Artificial para la mejora del Sistema Sanitario.

medical_image_segmentation

Medical image segmentation ( Eye vessel segmentation)

mib

Official code for Modeling the Background for Incremental Learning in Semantic Segmentation https://arxiv.org/abs/2002.00718

miccai-opensourcepapers

MICCAI 2019 Open Source Papers

mimo-unet

MIMO-UNet - Official Pytorch Implementation

miscnn

A framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning

missformer

mlp-mixer

Implementation for paper MLP-Mixer: An all-MLP Architecture for Vision

modern-computer-vision-with-pytorch

Modern Computer Vision with PyTorch, published by Packt

ms-cmr_miccai_2019

Analysis and modeling of the ventricles and myocardium are important in the diagnostic and treatment of heart diseases. Manual delineation of those tissues in cardiac MR (CMR) scans is laborious and time-consuming. The ambiguity of the boundaries makes the segmentation task rather challenging. Furthermore, the annotations on some modalities such as Late Gadolinium Enhancement (LGE) MRI, are often not available. We propose an end-to-end segmentation framework based on convolutional neural network (CNN) and adversarial learning. A dilated residual U-shape network is used as a segmentor to generate the prediction mask; meanwhile, a CNN is utilized as a discriminator model to judge the segmentation quality. To leverage the available annotations across modalities per patient, a new loss function named weak domain-transfer loss is introduced to the pipeline. The proposed model is evaluated on the public dataset released by the challenge organizer in MICCAI 2019, which consists of 45 sets of multi-sequence CMR images. We demonstrate that the proposed adversarial pipeline outperforms baseline deep-learning methods.

msma-net

mt-unet

Mixed Transformer UNet for Medical Image Segmentation

multiphase-active-contour-loss

“A Semisupervised Deep Learning-based Approach with Multiphase Active Contour Loss for Left Ventricle Segmentation from CMR Images,” The third International Conference on Sustainable Computing (SUSCOM-2021), 2021.

nasunet

nvm

Neural Virtual Machine

object-recognition

Computer Vision Practice --Object Recognition

palmnet

Source code for the 2019 IEEE TIFS paper "PalmNet: Gabor-PCA Convolutional Networks for Touchless Palmprint Recognition"

palmprint-feature-extraction-techniques

Various palmprint feature extraction techniques: CompCode, Local Tetra Patterns, RLOC