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Medical Image Analysis

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

https://github.com/UCSC-VLAA/MedTrinity-25M

공식 코드

MedTrinity-25M: A Large-scale Multimodal Dataset with Multigranular Annotations for Medicine

https://github.com/Valentyn1997/oct-diagn-semi-supervised

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Matching the Clinical Reality: Accurate OCT-Based Diagnosis From Few Labels

https://github.com/XinChenNottingham/SpatiallyConstrainedDCNN

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A Spatially Constrained Deep Convolutional Neural Network for Nerve Fiber Segmentation in Corneal Confocal Microscopic Images using Inaccurate Annotations

https://github.com/YaoZhang93/TumorCP

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TumorCP: A Simple but Effective Object-Level Data Augmentation for Tumor Segmentation

https://github.com/YazhouZhu19/RobustEMD

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RobustEMD: Domain Robust Matching for Cross-domain Few-shot Medical Image Segmentation

https://github.com/YingdaXia/SynthCP

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Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentation

https://github.com/Zch0414/3d-seeds

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Extending SEEDS to a Supervoxel Algorithm for Medical Image Analysis

https://github.com/ZhihuaLiuEd/SoTA-Brain-Tumor-Segmentation

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Deep Learning Based Brain Tumor Segmentation: A Survey

https://github.com/ZhiliangWu/mDKL

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Quantifying Predictive Uncertainty in Medical Image Analysis with Deep Kernel Learning

https://github.com/ZyoungXu/MoSt-DSA

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MoSt-DSA: Modeling Motion and Structural Interactions for Direct Multi-Frame Interpolation in DSA Images

https://github.com/aanandt/enhancing-covid-19-severity-analysis-through-ensemble-methods

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Enhancing COVID-19 Severity Analysis through Ensemble Methods

https://github.com/abachaa/3d-mir

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3D-MIR: A Benchmark and Empirical Study on 3D Medical Image Retrieval in Radiology

https://github.com/abdul2706/MaxViT-UNet

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MaxViT-UNet: Multi-Axis Attention for Medical Image Segmentation

https://github.com/adamtupper/medical-image-augmentation

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Analyzing Data Augmentation for Medical Images: A Case Study in Ultrasound Images

https://github.com/adgaudio/O-MedAL

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O-MedAL: Online Active Deep Learning for Medical Image Analysis

https://github.com/aecelaya/MIST

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PocketNet: A Smaller Neural Network for Medical Image Analysis

https://github.com/agis85/anatomy_modality_decomposition

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Disentangled Representation Learning in Cardiac Image Analysis

https://github.com/aiconslab/roodmri

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ROOD-MRI: Benchmarking the robustness of deep learning segmentation models to out-of-distribution and corrupted data in MRI

https://github.com/aigeeksgroup/sss

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SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation

https://github.com/ailab-kyunghee/mammi

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Mask-Free Neuron Concept Annotation for Interpreting Neural Networks in Medical Domain

https://github.com/aipmlab/facmic

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FACMIC: Federated Adaptative CLIP Model for Medical Image Classification

https://github.com/aipmlab/kd-fmv

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A Knowledge Distillation-Based Approach to Enhance Transparency of Classifier Models

https://github.com/airlab-unibas/airlab

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AirLab: Autograd Image Registration Laboratory

https://github.com/ajay-pratap-singh-518/Capsule-Vision-2024-challenge

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Capsule Vision 2024 Challenge: Multi-Class Abnormality Classification for Video Capsule Endoscopy

https://github.com/ajoshiusc/brainseg

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Learning from imperfect training data using a robust loss function: application to brain image segmentation

https://github.com/aleemsidra/augmentation-based-generalized-enhancement

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Random Data Augmentation based Enhancement: A Generalized Enhancement Approach for Medical Datasets

https://github.com/aleesuss/c19

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Explainable and Lightweight Model for COVID-19 Detection Using Chest Radiology Images

https://github.com/alexander-rakhlin/ICIAR2018

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Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis

https://github.com/alexyouxin/explicit-shape-priors

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Learning with Explicit Shape Priors for Medical Image Segmentation

https://github.com/alibaba-damo-academy/alice

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Anatomical Invariance Modeling and Semantic Alignment for Self-supervised Learning in 3D Medical Image Analysis

https://github.com/alibaba-damo-academy/same

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SAME++: A Self-supervised Anatomical eMbeddings Enhanced medical image registration framework using stable sampling and regularized transformation

https://github.com/alibaba-damo-academy/self-supervised-anatomical-embedding-v2

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SAM: Self-supervised Learning of Pixel-wise Anatomical Embeddings in Radiological Images

https://github.com/alison-brie/autoreg

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Automated Learning for Deformable Medical Image Registration by Jointly Optimizing Network Architectures and Objective Functions

https://github.com/alperctnkaya/fedaug

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Improving Performance of Federated Learning based Medical Image Analysis in Non-IID Settings using Image Augmentation

https://github.com/ameliajimenez/capsule-networks-medical-data-challenges

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Capsule Networks against Medical Imaging Data Challenges

https://github.com/amirhossein-kz/awesome-diffusion-models-in-medical-imaging

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Diffusion Models for Medical Image Analysis: A Comprehensive Survey

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