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Anatomy

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/luo-yihao/doph

Explicit Differentiable Slicing and Global Deformation for Cardiac Mesh Reconstruction

https://github.com/lyqcom/2D-unet

Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem

https://github.com/mathiasunberath/DeepDRR

X-ray-transform Invariant Anatomical Landmark Detection for Pelvic Trauma Surgery

https://github.com/medical-image-analysis-laboratory/fetalsynthseg

Brain-ID: Learning Contrast-agnostic Anatomical Representations for Brain Imaging

https://github.com/miyosuda/snmf

A Hebbian/Anti-Hebbian Network Derived from Online Non-Negative Matrix Factorization Can Cluster and Discover Sparse Features

https://github.com/monarch-initiative/curate-gpt

MapperGPT: Large Language Models for Linking and Mapping Entities

https://github.com/mr-hosseinzadehtaher/eden

Towards Foundation Models Learned from Anatomy in Medical Imaging via Self-Supervision

https://github.com/mrgiovanni/dissertation

Models Genesis

https://github.com/naamiinepal/xrayto3d-benchmark

Benchmarking Encoder-Decoder Architectures for Biplanar X-ray to 3D Shape Reconstruction

https://github.com/neuropoly/axondeepseg

Deep Active Learning for Axon-Myelin Segmentation on Histology Data

https://github.com/oubino/General-location-finding

Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy

https://github.com/point0bar1/ebm-anatomy

On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models

https://github.com/pulkit-khandelwal/picsl-ex-vivo-segmentation

Automated deep learning segmentation of high-resolution 7 T postmortem MRI for quantitative analysis of structure-pathology correlations in neurodegenerative diseases

https://github.com/qmeng99/Multi-task-Representation-Disentanglement

Representation Disentanglement for Multi-task Learning with application to Fetal Ultrasound

https://github.com/raidionics/raidionics-slicer

AeroPath: An airway segmentation benchmark dataset with challenging pathology

https://github.com/rvlopes/gloria

Scaling Language Models: Methods, Analysis & Insights from Training Gopher

https://github.com/saalfeldlab/z-spacing

Post-acquisition image based compensation for thickness variation in microscopy section series

https://github.com/the-veeraraghavan-lab/smit

Self-supervised 3D anatomy segmentation using self-distilled masked image transformer (SMIT)

https://github.com/vios-s/csdisentanglement_metrics_library

Disentangled Representation Learning in Cardiac Image Analysis

https://github.com/voxelmorph/voxelmorph

Learning Conditional Deformable Templates with Convolutional Networks

https://github.com/wdika/mridc

Recurrent Variational Network: A Deep Learning Inverse Problem Solver applied to the task of Accelerated MRI Reconstruction

https://github.com/wentaozhu/AnatomyNet-for-anatomical-segmentation

AnatomyNet: Deep Learning for Fast and Fully Automated Whole-volume Segmentation of Head and Neck Anatomy

https://github.com/yannbouteiller/rtgym

Reinforcement Learning with Random Delays

https://github.com/ydzhang-stormstout/HAT

Hyperbolic Graph Attention Network

https://github.com/yuki3-18/Topological-DVAE

A Topological Loss Function for Deep-Learning based Image Segmentation using Persistent Homology

https://github.com/yuxiang-lai117/med-r1

Med-R1: Reinforcement Learning for Generalizable Medical Reasoning in Vision-Language Models

https://github.com/z-fabian/MRAugment

End-to-End Variational Networks for Accelerated MRI Reconstruction

https://github.com/zhaoziheng/sat

One Model to Rule them All: Towards Universal Segmentation for Medical Images with Text Prompts

https://github.com/zhaozsq/airway_segmentation

Learning Tubule-Sensitive CNNs for Pulmonary Airway and Artery-Vein Segmentation in CT

https://github.com/zherenz/imt-mrd

Imaging transformer for MRI denoising with the SNR unit training: enabling generalization across field-strengths, imaging contrasts, and anatomy

https://gitlab.com/matzkin/deep-brain-extractor

Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.