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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/fyc423/sre_unsupervised_segm

공식 코드

Equivariant Imaging Biomarkers for Robust Unsupervised Segmentation of Histopathology

https://github.com/garrettjenkinson/condor_pytorch

공식 코드

Universally Rank Consistent Ordinal Regression in Neural Networks

https://github.com/garrettjenkinson/condor_tensorflow

공식 코드

Universally Rank Consistent Ordinal Regression in Neural Networks

https://github.com/gledsonrt/pigpdynamicmodalloadreconstruction

공식 코드

Efficient dynamic modal load reconstruction using physics-informed Gaussian processes based on frequency-sparse Fourier basis functions

https://github.com/glejdis/whole-slide-mil-for-predicting-axillary-lymph-node-metastasis

공식 코드

Whole Slide Multiple Instance Learning for Predicting Axillary Lymph Node Metastasis

https://github.com/hamish-haggerty/base_rbt

공식 코드

Self-supervised learning for skin cancer diagnosis with limited training data

https://github.com/haranrk/DigiPathAI

공식 코드

A Generalized Deep Learning Framework for Whole-Slide Image Segmentation and Analysis

https://github.com/helenypzhang/subspace-multimodal-learning

공식 코드

Knowledge-driven Subspace Fusion and Gradient Coordination for Multi-modal Learning

https://github.com/hieuphamha19/csds

공식 코드

Learning Disentangled Stain and Structural Representations for Semi-Supervised Histopathology Segmentation

https://github.com/hilab-git/segrap2023

공식 코드

SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma

https://github.com/histocartography/hact-net

공식 코드

Hierarchical Graph Representations in Digital Pathology

https://github.com/hku-medai/conslide

공식 코드

ConSlide: Asynchronous Hierarchical Interaction Transformer with Breakup-Reorganize Rehearsal for Continual Whole Slide Image Analysis

https://github.com/hku-medai/higt

공식 코드

HIGT: Hierarchical Interaction Graph-Transformer for Whole Slide Image Analysis

https://github.com/huahuiyi/mmdp

공식 코드

Unified Modeling Enhanced Multimodal Learning for Precision Neuro-Oncology

https://github.com/huaxiuyao/wild-time

공식 코드

Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time

https://github.com/irohxu/pie

공식 코드

PIE: Simulating Disease Progression via Progressive Image Editing

https://github.com/jamesrenhoulee/CancerNet-SCa

공식 코드

CancerNet-SCa: Tailored Deep Neural Network Designs for Detection of Skin Cancer from Dermoscopy Images

https://github.com/jgonzalezab/cvae-pp-downscaling

공식 코드

On the use of Deep Generative Models for Perfect Prognosis Climate Downscaling

https://github.com/jgonzalezab/xai-metrics-north-america

공식 코드

Using Explainability to Inform Statistical Downscaling Based on Deep Learning Beyond Standard Validation Approaches

https://github.com/jiazhang93/segmentation

공식 코드

Whole-Body Lesion Segmentation in 18F-FDG PET/CT

https://github.com/jieWeiLu/Strabismus-Detection-for-Telemedicine-Application

공식 코드

Automated Strabismus Detection for Telemedicine Applications

https://github.com/jmnolte/hccnet

공식 코드

A CNN-Transformer for Classification of Longitudinal 3D MRI Images -- A Case Study on Hepatocellular Carcinoma Prediction

https://github.com/jsb-ucla/itca

공식 코드

Information-theoretic Classification Accuracy: A Criterion that Guides Data-driven Combination of Ambiguous Outcome Labels in Multi-class Classification

https://github.com/jsh0792/mcti

공식 코드

Multimodal Cross-Task Interaction for Survival Analysis in Whole Slide Pathological Images

https://github.com/jsliam94/cmswinkan

공식 코드

Towards Accurate and Interpretable Neuroblastoma Diagnosis via Contrastive Multi-scale Pathological Image Analysis

https://github.com/junfish/Awesome-Multitask-Learning

공식 코드

Unleashing the Power of Multi-Task Learning: A Comprehensive Survey Spanning Traditional, Deep, and Pretrained Foundation Model Eras

https://github.com/junjianli106/mico

공식 코드

MiCo: Multiple Instance Learning with Context-Aware Clustering for Whole Slide Image Analysis

https://github.com/knowlab/daindex-framework

공식 코드

Quantifying Health Inequalities Induced by Data and AI Models

https://github.com/lbasyal/Histopathologic-Cancer-Detection-

공식 코드

Histopathologic Cancer Detection

https://github.com/learner-codec/autoprom_sam

공식 코드

Pathological Primitive Segmentation Based on Visual Foundation Model with Zero-Shot Mask Generation

https://github.com/leomlck/medimp

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MEDIMP: 3D Medical Images with clinical Prompts from limited tabular data for renal transplantation

https://github.com/leooo-shen/tvrl

공식 코드

Spatiotemporal Representation Learning for Short and Long Medical Image Time Series

https://github.com/lhy1007/m3c2

공식 코드

Joint Modelling Histology and Molecular Markers for Cancer Classification

https://github.com/liupei101/DSCA

공식 코드

DSCA: A Dual-Stream Network with Cross-Attention on Whole-Slide Image Pyramids for Cancer Prognosis

https://github.com/liupei101/advmil

공식 코드

AdvMIL: Adversarial Multiple Instance Learning for the Survival Analysis on Whole-Slide Images

https://github.com/liupei101/vlsa

공식 코드

Interpretable Vision-Language Survival Analysis with Ordinal Inductive Bias for Computational Pathology

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