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Prognosis

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

https://github.com/lkpengcs/syntumoranalyzer

공식 코드

Optimizing Synthetic Data for Enhanced Pancreatic Tumor Segmentation

https://github.com/lmxmercy/mgct

공식 코드

MGCT: Mutual-Guided Cross-Modality Transformer for Survival Outcome Prediction using Integrative Histopathology-Genomic Features

https://github.com/lucas-rbnt/drim

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DRIM: Learning Disentangled Representations from Incomplete Multimodal Healthcare Data

https://github.com/luchang-cs/chet

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Context-aware Health Event Prediction via Transition Functions on Dynamic Disease Graphs

https://github.com/ma3oun/gbheterogeneity

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Uncovering the Genetic Basis of Glioblastoma Heterogeneity through Multimodal Analysis of Whole Slide Images and RNA Sequencing Data

https://github.com/mahmoodlab/FISH

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Fast and Scalable Image Search For Histology

https://github.com/mahmoodlab/PORPOISE

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Pan-Cancer Integrative Histology-Genomic Analysis via Interpretable Multimodal Deep Learning

https://github.com/mahmoodlab/PathomicFusion

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Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis

https://github.com/mahmoodlab/mamba

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Weakly Supervised AI for Efficient Analysis of 3D Pathology Samples

https://github.com/mahmoodlab/survpath

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Modeling Dense Multimodal Interactions Between Biological Pathways and Histology for Survival Prediction

https://github.com/mahmoodlab/titan

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Multimodal Whole Slide Foundation Model for Pathology

https://github.com/marrlab/ae-cfe

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Unsupervised Cross-Domain Feature Extraction for Single Blood Cell Image Classification

https://github.com/mateoespinosa/remix

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Efficient Decompositional Rule Extraction for Deep Neural Networks

https://github.com/matteochieregato/GradientboostingCovid19

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A hybrid machine learning/deep learning COVID-19 severity predictive model from CT images and clinical data

https://github.com/maxhallemeesch/praline

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Prior Knowledge Injection into Deep Learning Models Predicting Gene Expression from Whole Slide Images

https://github.com/mdciri/Vox2Vox

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Vox2Vox: 3D-GAN for Brain Tumour Segmentation

https://github.com/mdsatria/multiattentionmil

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Multi-Scale Attention-based Multiple Instance Learning for Classification of Multi-Gigapixel Histology Images

https://github.com/menggersherry/gene-moe

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Gene-MOE: A sparsely gated prognosis and classification framework exploiting pan-cancer genomic information

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/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

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