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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/rohit-kundu/ideal-icassp23

공식 코드

IDEAL: Improved DEnse locAL Contrastive Learning for Semi-Supervised Medical Image Segmentation

https://github.com/roldbach/autoencoder-3d-ct-sisr

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Is Autoencoder Truly Applicable for 3D CT Super-Resolution?

https://github.com/rpidial/disease-informed-vlm-adaptation

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Disease-informed Adaptation of Vision-Language Models

https://github.com/rudylyh/CTN_data

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Contour Transformer Network for One-shot Segmentation of Anatomical Structures

https://github.com/rui-yan/ssl-fl

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Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging

https://github.com/ruidaniel/rbaca

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Continual Deep Active Learning for Medical Imaging: Replay-Base Architecture for Context Adaptation

https://github.com/ruizhe-l/semi-segmentation

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A generic ensemble based deep convolutional neural network for semi-supervised medical image segmentation

https://github.com/ruizhe-l/tgb-gan

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Image Augmentation Using a Task Guided Generative Adversarial Network for Age Estimation on Brain MRI

https://github.com/rundherum/pymia

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pymia: A Python package for data handling and evaluation in deep learning-based medical image analysis

https://github.com/runshi-zhang/utsrmorph

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UTSRMorph: A Unified Transformer and Superresolution Network for Unsupervised Medical Image Registration

https://github.com/sadimanna/skid

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SKID: Self-Supervised Learning for Knee Injury Diagnosis from MRI Data

https://github.com/sadimanna/sslm

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SKID: Self-Supervised Learning for Knee Injury Diagnosis from MRI Data

https://github.com/saintslab/pepr

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PePR: Performance Per Resource Unit as a Metric to Promote Small-Scale Deep Learning in Medical Image Analysis

https://github.com/sanofi-public/miphei-vit

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MIPHEI-ViT: Multiplex Immunofluorescence Prediction from H&E Images using ViT Foundation Models

https://github.com/sbasu276/radformer

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RadFormer: Transformers with Global-Local Attention for Interpretable and Accurate Gallbladder Cancer Detection

https://github.com/scan-nrad/e3nn_unet

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Leveraging SO(3)-steerable convolutions for pose-robust semantic segmentation in 3D medical data

https://github.com/schuture/metalr

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MetaLR: Meta-tuning of Learning Rates for Transfer Learning in Medical Imaging

https://github.com/sdoerrich97/unoranic

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unORANIC: Unsupervised Orthogonalization of Anatomy and Image-Characteristic Features

https://github.com/sdoerrich97/unoranic-plus

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Unsupervised Feature Orthogonalization for Learning Distortion-Invariant Representations

https://github.com/sharif-apu/DPE_JBHI

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Deep Perceptual Enhancement for Medical Image Analysis

https://github.com/shayanfazeli/marrovision

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Beyond Labels: Visual Representations for Bone Marrow Cell Morphology Recognition

https://github.com/simonHad/breast-cancer-detection-using-convolutional-neural-networks

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Breast Cancer Detection Using Convolutional Neural Networks

https://github.com/simonustc/mcpa-for-2d-medical-image-segmentation

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MCPA: Multi-scale Cross Perceptron Attention Network for 2D Medical Image Segmentation

https://github.com/simula/Kvasir-VQA

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Kvasir-VQA: A Text-Image Pair GI Tract Dataset

https://github.com/siyuanyan1/pldg

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Prompt-driven Latent Domain Generalization for Medical Image Classification

https://github.com/skyz215/proco

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ProCo: Prototype-aware Contrastive Learning for Long-tailed Medical Image Classification

https://github.com/sldgroup/emcad

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EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation

https://github.com/sllambias/yucca

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Yucca: A Deep Learning Framework For Medical Image Analysis

https://github.com/snavalm/mood22

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Achieving state-of-the-art performance in the Medical Out-of-Distribution (MOOD) challenge using plausible synthetic anomalies

https://github.com/solemoon/xmorpher

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XMorpher: Full Transformer for Deformable Medical Image Registration via Cross Attention

https://github.com/sooperset/boss

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Bayesian Optimization Meets Self-Distillation

https://github.com/soumitri2001/smalldatassl

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Exploring Self-Supervised Representation Learning For Low-Resource Medical Image Analysis

https://github.com/sri-karthik-avala/capsule-challenge-2024

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

https://github.com/ss-sun/attri-net

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Inherently Interpretable Multi-Label Classification Using Class-Specific Counterfactuals

https://github.com/stanfordmlgroup/chexpert-labeler

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Structured dataset documentation: a datasheet for CheXpert

https://github.com/stefanowoerner/medimeta-fsl-benchmark

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Navigating Data Scarcity using Foundation Models: A Benchmark of Few-Shot and Zero-Shot Learning Approaches in Medical Imaging

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