Papers with Code 아카이브

Predictive Process Monitoring

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 1
평가표: 0
보존된 평가 기록: 0

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/estfernandez/Slide_Image_Segmentation_and_Extraction

공식 코드

Discovering Clinically Meaningful Shape Features for the Analysis of Tumor Pathology Images

https://github.com/ezequieldlrosa/isles22

공식 코드

A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge

https://github.com/facebookresearch/CovidPrognosis

공식 코드

COVID-19 Prognosis via Self-Supervised Representation Learning and Multi-Image Prediction

https://github.com/faildeny/debiasing-cardiac-mri

공식 코드

Debiasing Cardiac Imaging with Controlled Latent Diffusion Models

https://github.com/farbodtaymouri/GanPredictiveMonitoring

공식 코드

Predictive Business Process Monitoring via Generative Adversarial Nets: The Case of Next Event Prediction

https://github.com/fiy2w/mri_seq2seq

공식 코드

An Explainable Deep Framework: Towards Task-Specific Fusion for Multi-to-One MRI Synthesis

https://github.com/fjr9516/synthetic-brain-aging

공식 코드

Generative Aging of Brain Images with Diffeomorphic Registration

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/gbrltv/business_process_encoding

공식 코드

Trace Encoding in Process Mining: a survey and benchmarking

https://github.com/ghadaelkhawaga/ppm_xai_comparison

공식 코드

Explainability of Predictive Process Monitoring Results: Can You See My Data Issues?

https://github.com/ghksdl6025/online_ppm_stability

공식 코드

Measuring the Stability of Process Outcome Predictions in Online Settings

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/hansweytjens/uncertainty

공식 코드

Learning Uncertainty with Artificial Neural Networks for Improved Predictive Process Monitoring

https://github.com/hansweytjens/uncertainty-remaining_time

공식 코드

Learning Uncertainty with Artificial Neural Networks for Improved Remaining Time Prediction of Business Processes

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/irhete/predictive-monitoring-benchmark

공식 코드

Outcome-Oriented Predictive Process Monitoring: Review and Benchmark

https://github.com/irhete/stability-predictive-monitoring

공식 코드

Temporal Stability in Predictive Process Monitoring

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/jaripeeperkorn/Group-Fairness-in-Predictive-Process-Monitoring

공식 코드

Achieving Group Fairness through Independence in Predictive Process Monitoring

https://github.com/jaripeeperkorn/generalizationppm

공식 코드

Can deep neural networks learn process model structure? An assessment framework and analysis

https://github.com/jaripeeperkorn/lstm_process_model_structure

공식 코드

Can recurrent neural networks learn process model structure?

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

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