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/rezazad68/smunet

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SMU-Net: Style matching U-Net for brain tumor segmentation with missing modalities

https://github.com/rixez/brats21_kaist_mri_lab

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Extending nn-UNet for brain tumor segmentation

https://github.com/rohdelab/cell-image-classification

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Cell image classification: a comparative overview

https://github.com/rustins/crossfusion

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CrossFusion: A Multi-Scale Cross-Attention Convolutional Fusion Model for Cancer Survival Prediction

https://github.com/ryandok/jpa

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Exploring Kinetic Curves Features for the Classification of Benign and Malignant Breast Lesions in DCE-MRI

https://github.com/samadeusfp/alarmBasedPrescriptiveProcessMonitoring

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Fire Now, Fire Later: Alarm-Based Systems for Prescriptive Process Monitoring

https://github.com/sbhattacharyay/dynamic_gose_model

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Mining the contribution of intensive care clinical course to outcome after traumatic brain injury

https://github.com/sbhattacharyay/ordinal_GOSE_prediction

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The leap to ordinal: detailed functional prognosis after traumatic brain injury with a flexible modelling approach

https://github.com/schroeterjulien/midl-2022-segmentation-consistent-lesion-counting

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Segmentation-Consistent Probabilistic Lesion Counting

https://github.com/scjjb/ovcareview

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Artificial Intelligence in Ovarian Cancer Histopathology: A Systematic Review

https://github.com/scxsunchenxi/ccts

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Continuous Diagnosis and Prognosis by Controlling the Update Process of Deep Neural Networks

https://github.com/sergiormpereira/brain_tumor_grading

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Automatic brain tumor grading from MRI data using convolutional neural networks and quality assessment

https://github.com/soumitri2001/smalldatassl

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Exploring Self-Supervised Representation Learning For Low-Resource Medical Image Analysis

https://github.com/ssikjeong1/Deep_Recurrent_AD

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Deep Recurrent Model for Individualized Prediction of Alzheimer's Disease Progression

https://github.com/stanfordmlgroup/DLBCL-Morph

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DLBCL-Morph: Morphological features computed using deep learning for an annotated digital DLBCL image set

https://github.com/sukjulian/coronary-mesh-convolution

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Mesh Neural Networks for SE(3)-Equivariant Hemodynamics Estimation on the Artery Wall

https://github.com/sukjulian/segnn-hemodynamics

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SE(3) symmetry lets graph neural networks learn arterial velocity estimation from small datasets

https://github.com/swiman/mamba-medical-classification

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Classification of Gleason Grading in Prostate Cancer Histopathology Images Using Deep Learning Techniques: YOLO, Vision Transformers, and Vision Mamba

https://github.com/tabrisrei/isles22_ensemble

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

https://github.com/tangwen920812/mmmna-net

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MMMNA-Net for Overall Survival Time Prediction of Brain Tumor Patients

https://github.com/tanyanair/segmentation_uncertainty

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Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation

https://github.com/theRuchiChauhan/biomarker-prediction-breast-cancer

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Exploring Genetic-histologic Relationships in Breast Cancer

https://github.com/thomastkok/active-selection-of-classification-features

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Active Selection of Classification Features

https://github.com/tkforgeron/ocppm

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HOEG: A New Approach for Object-Centric Predictive Process Monitoring

https://github.com/tobiasoh/master_thesis

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Bayesian Cox model with graph-structured variable selection priors for multi-omics biomarker identification

https://github.com/toologicbv/cardiacSegUncertainty

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Automatic segmentation with detection of local segmentation failures in cardiac MRI

https://github.com/ugurdemir/tranformer_liver_segmentation

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Transformer based Generative Adversarial Network for Liver Segmentation

https://github.com/uit-hdl/histology

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A Pragmatic Machine Learning Approach to Quantify Tumor Infiltrating Lymphocytes in Whole Slide Images

https://github.com/vlbthambawita/hecktor_2022_mlc

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MLC at HECKTOR 2022: The Effect and Importance of Training Data when Analyzing Cases of Head and Neck Tumors using Machine Learning

https://github.com/vollmer-lab/survival_fairness

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Flexible Group Fairness Metrics for Survival Analysis

https://github.com/wizaca23/BBBC006-Instance-Segmentation

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Enforcing Morphological Information in Fully Convolutional Networks to Improve Cell Instance Segmentation in Fluorescence Microscopy Images

https://github.com/xmed-lab/mssg

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Morphology-inspired Unsupervised Gland Segmentation via Selective Semantic Grouping

https://github.com/xmed-lab/oeem

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Online Easy Example Mining for Weakly-supervised Gland Segmentation from Histology Images

https://github.com/yashsharma/mani

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MaNi: Maximizing Mutual Information for Nuclei Cross-Domain Unsupervised Segmentation

https://github.com/yixinghuang/chatgpt-benchmark-on-radiation-oncology

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Benchmarking ChatGPT-4 on ACR Radiation Oncology In-Training (TXIT) Exam and Red Journal Gray Zone Cases: Potentials and Challenges for AI-Assisted Medical Education and Decision Making in Radiation Oncology

https://github.com/yixinghuang/deepmedicplus

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Deep learning for brain metastasis detection and segmentation in longitudinal MRI data

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