Papers with Code 아카이브

Disease Prediction

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코드

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

https://github.com/ABILab-CUHK/VisionFM

공식 코드

VisionFM: a Multi-Modal Multi-Task Vision Foundation Model for Generalist Ophthalmic Artificial Intelligence

https://github.com/EmreTaha/TINC

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TINC: Temporally Informed Non-Contrastive Learning for Disease Progression Modeling in Retinal OCT Volumes

https://github.com/JinYinzhu/featurecollapse

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Measuring Feature Dependency of Neural Networks by Collapsing Feature Dimensions in the Data Manifold

https://github.com/KaimingHe/resnet-1k-layers

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Deep Residual Learning for Image Recognition

https://github.com/KarolAntczak/multimetapath2vec

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Representation Learning for Medical Data

https://github.com/MASILab/BRAID

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Brain age identification from diffusion MRI synergistically predicts neurodegenerative disease

https://github.com/MMIV-ML/DLMI2018

공식 코드

An overview of deep learning in medical imaging focusing on MRI

https://github.com/MostHumble/PatientTrajectoryForecasting

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Patient Trajectory Prediction: Integrating Clinical Notes with Transformers

https://github.com/NYUMedML/DeepEHR

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Deep EHR: Chronic Disease Prediction Using Medical Notes

https://github.com/SeojinBang/MKKC

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Robust Multiple Kernel k-means Clustering using Min-Max Optimization

https://github.com/SharifAmit/OCT_Classification

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Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images

https://github.com/SharifAmit/Robust_Joint_Attention

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Improving Robustness using Joint Attention Network For Detecting Retinal Degeneration From Optical Coherence Tomography Images

https://github.com/SharifAmit/VTGAN

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VTGAN: Semi-supervised Retinal Image Synthesis and Disease Prediction using Vision Transformers

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/ZhiGroup/Med-BERT

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Med-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease prediction

https://github.com/ZhixiangSu/PoMP

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Enabling Patient-side Disease Prediction via the Integration of Patient Narratives

https://github.com/anotherkaizhang/ms

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Predicting multiple sclerosis disease severity with multimodal deep neural networks

https://github.com/astorfi/cor-gan

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CorGAN: Correlation-Capturing Convolutional Generative Adversarial Networks for Generating Synthetic Healthcare Records

https://github.com/chantalmp/unsupervised-pre-training-on-patient-population-graphs-for-patient-level-predictions

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Unsupervised Pre-Training on Patient Population Graphs for Patient-Level Predictions

https://github.com/clinfo/ood4tab

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Out-of-distribution Reject Option Method for Dataset Shift Problem in Early Disease Onset Prediction

https://github.com/cmthwal/FL-Attention-CDSS

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Attention on Personalized Clinical Decision Support System: Federated Learning Approach

https://github.com/cristianopatricio/2-step-concept-based-skin-diagnosis

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A Two-Step Concept-Based Approach for Enhanced Interpretability and Trust in Skin Lesion Diagnosis

https://github.com/cunningham-lab/augcoda

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Data Augmentation for Compositional Data: Advancing Predictive Models of the Microbiome

https://github.com/dipkamal/chestxrayclassifier

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Multi-Label Classification of Thoracic Diseases using Dense Convolutional Network on Chest Radiographs

https://github.com/dylan-slack/talktomodel

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TalkToModel: Explaining Machine Learning Models with Interactive Natural Language Conversations

https://github.com/fragirla/mmssl-for-cvd-pred

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Enhancing Cardiovascular Disease Prediction through Multi-Modal Self-Supervised Learning

https://github.com/francesco-sovrano/yai4hu

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Generating User-Centred Explanations via Illocutionary Question Answering: From Philosophy to Interfaces

https://github.com/frankkramer-lab/riadd.aucmedi

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Multi-Disease Detection in Retinal Imaging based on Ensembling Heterogeneous Deep Learning Models

https://github.com/hennyjie/ibgnn

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BrainNNExplainer: An Interpretable Graph Neural Network Framework for Brain Network based Disease Analysis

https://github.com/jcreinhold/counterfactualms

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A Structural Causal Model for MR Images of Multiple Sclerosis

https://github.com/ku-milab/licol

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A Quantitatively Interpretable Model for Alzheimer's Disease Prediction Using Deep Counterfactuals

https://github.com/kwanwaichung/coad

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CoAD: Automatic Diagnosis through Symptom and Disease Collaborative Generation

https://github.com/lcosmo/DGM_pytorch

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Differentiable Graph Module (DGM) for Graph Convolutional Networks

https://github.com/luxtu/OCTA-graph

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Interpretable Retinal Disease Prediction Using Biology-Informed Heterogeneous Graph Representations

https://github.com/mahsa91/GKD

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GKD: Semi-supervised Graph Knowledge Distillation for Graph-Independent Inference

https://github.com/mahsa91/RA-GCN

공식 코드

RA-GCN: Graph Convolutional Network for Disease Prediction Problems with Imbalanced Data

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