Papers with Code 아카이브

Predictive Process Monitoring

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A branch of predictive analysis that attempts to predict some future state of a business process.

코드

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

https://github.com/mhabijan/medical_images_generation

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Generation of Artificial CT Images using Patch-based Conditional Generative Adversarial Networks

https://github.com/miclab-unicamp/medpseg

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MEDPSeg: Hierarchical polymorphic multitask learning for the segmentation of ground-glass opacities, consolidation, and pulmonary structures on computed tomography

https://github.com/mileswyn/ich_mllms_validation

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Zero-Shot Multi-modal Large Language Model v.s. Supervised Deep Learning: A Comparative Study on CT-Based Intracranial Hemorrhage Subtyping

https://github.com/mipt-oulu/climat

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CLIMAT: Clinically-Inspired Multi-Agent Transformers for Knee Osteoarthritis Trajectory Forecasting

https://github.com/miriamcobo/multitasklearning_ich_prognosis

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Multi-task Learning Approach for Intracranial Hemorrhage Prognosis

https://github.com/mkaep/transformer-explainability

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Attention Please: What Transformer Models Really Learn for Process Prediction

https://github.com/mobarakol/GBM_Prognosis

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Glioblastoma Multiforme Prognosis: MRI Missing Modality Generation, Segmentation and Radiogenomic Survival Prediction

https://github.com/moeinsorkhei/csaw-m_annotation_tool

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CSAW-M: An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer

https://github.com/monkeygobah/bone-metastasis

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Prediction of Lung Metastasis from Hepatocellular Carcinoma using the SEER Database

https://github.com/mskcc-computational-pathology/dmmn-osteosarcoma

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Deep Learning-Based Objective and Reproducible Osteosarcoma Chemotherapy Response Assessment and Outcome Prediction

https://github.com/mu-data-science/gaf

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Predicting Survivability of Cancer Patients with Metastatic Patterns Using Explainable AI

https://github.com/mungomeng/survival-adamss

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AdaMSS: Adaptive Multi-Modality Segmentation-to-Survival Learning for Survival Outcome Prediction from PET/CT Images

https://github.com/mungomeng/survival-deepmss

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AdaMSS: Adaptive Multi-Modality Segmentation-to-Survival Learning for Survival Outcome Prediction from PET/CT Images

https://github.com/naamiinepal/medvlsm

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Exploring Transfer Learning in Medical Image Segmentation using Vision-Language Models

https://github.com/nabavilab/ufcn

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Unsupversied feature correlation model to predict breast abnormal variation maps in longitudinal mammograms

https://github.com/nmduonggg/fddm

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Bridging Classification and Segmentation in Osteosarcoma Assessment via Foundation and Discrete Diffusion Models

https://github.com/nrc-cnrc/COVID-US

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COVIDx-US -- An open-access benchmark dataset of ultrasound imaging data for AI-driven COVID-19 analytics

https://github.com/nubagcilab/peri-pancreatic-edema-detection

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Detection of Peri-Pancreatic Edema using Deep Learning and Radiomics Techniques

https://github.com/numanai/biomedia-hecktor2021

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An Ensemble Approach for Patient Prognosis of Head and Neck Tumor Using Multimodal Data

https://github.com/numanai/survrnc

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SurvRNC: Learning Ordered Representations for Survival Prediction using Rank-N-Contrast

https://github.com/nyuad-cai/COVID19Complications

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Clinical prediction system of complications among COVID-19 patients: a development and validation retrospective multicentre study

https://github.com/nyukat/COVID-19_prognosis

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An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department

https://github.com/omniaalwazzan/moab

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MOAB: Multi-Modal Outer Arithmetic Block For Fusion Of Histopathological Images And Genetic Data For Brain Tumor Grading

https://github.com/oulu-imeds/climatv2

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Clinically-Inspired Multi-Agent Transformers for Disease Trajectory Forecasting from Multimodal Data

https://github.com/perceivelab/feder

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FedER: Federated Learning through Experience Replay and Privacy-Preserving Data Synthesis

https://github.com/pokeblow/bls-gan

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BLS-GAN: A Deep Layer Separation Framework for Eliminating Bone Overlap in Conventional Radiographs

https://github.com/precision-vision/oed-classification-segmentation

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An Attention Based Pipeline for Identifying Pre-Cancer Lesions in Head and Neck Clinical Images

https://github.com/puzzled-hui/atm-22-related-work

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AeroPath: An airway segmentation benchmark dataset with challenging pathology

https://github.com/quiil/falformer

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FALFormer: Feature-aware Landmarks self-attention for Whole-slide Image Classification

https://github.com/rachitsaluja/pathrep-bench

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Cancer Type, Stage and Prognosis Assessment from Pathology Reports using LLMs

https://github.com/raidionics/aeropath

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AeroPath: An airway segmentation benchmark dataset with challenging pathology

https://github.com/raidionics/raidionics

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Raidionics: an open software for pre- and postoperative central nervous system tumor segmentation and standardized reporting

https://github.com/ransulab/eocprognosis

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EOCSA: Predicting Prognosis of Epithelial Ovarian Cancer with Whole Slide Histopathological Images

https://github.com/raymvp/hacsurv

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HACSurv: A Hierarchical Copula-Based Approach for Survival Analysis with Dependent Competing Risks

https://github.com/renato145/posthocOS

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Post-hoc Overall Survival Time Prediction from Brain MRI

https://github.com/rezacsedu/XAI_Cancer_Prediction

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OncoNetExplainer: Explainable Predictions of Cancer Types Based on Gene Expression Data

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