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Cancer

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

https://github.com/lishen/end2end-all-conv

공식 코드

Deep Learning to Improve Breast Cancer Early Detection on Screening Mammography

https://github.com/longman-stan/skindistilvit

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SkinDistilViT: Lightweight Vision Transformer for Skin Lesion Classification

https://github.com/martabuetas/enhancingbreastcancerdiagnosis

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Mitigating annotation shift in cancer classification using single image generative models

https://github.com/matinamehdizadeh/Breast-Cancer-Detection

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Classification of Breast Cancer Histopathology Images using a Modified Supervised Contrastive Learning Method

https://github.com/mazurowski-lab/duke-dbt-data

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A Multi-Modal AI System for Screening Mammography: Integrating 2D and 3D Imaging to Improve Breast Cancer Detection in a Prospective Clinical Study

https://github.com/medgift/iMIMIC-RCVs

공식 코드

Regression Concept Vectors for Bidirectional Explanations in Histopathology

https://github.com/mohammadAbbasniya/BreastCancer-Classification

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Classification of Breast Tumours Based on Histopathology Images Using Deep Features and Ensemble of Gradient Boosting Methods

https://github.com/netherlands-cancer-institute/ce-mri

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Synthesis of Contrast-Enhanced Breast MRI Using Multi-b-Value DWI-based Hierarchical Fusion Network with Attention Mechanism

https://github.com/niuchuangnn/m3fm

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Specialty-Oriented Generalist Medical AI for Chest CT Screening

https://github.com/nyukat/BIRADS_classifier

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High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks

https://github.com/nyukat/GMIC

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An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization

https://github.com/nyukat/breast_cancer_classifier

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Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening

https://github.com/nyukat/mammography_metarepository

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Meta-repository of screening mammography classifiers

https://github.com/nyukat/perception_comparison

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Differences between human and machine perception in medical diagnosis

https://github.com/pbevan1/Skin-Deep-Unlearning

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Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification

https://github.com/pohl-michel/Lucas-Kanade-pyramidal-optical-flow-for-3D-image-sequences

공식 코드

Prediction of the motion of chest internal points using a recurrent neural network trained with real-time recurrent learning for latency compensation in lung cancer radiotherapy

https://github.com/pohl-michel/time-series-forecasting-with-UORO-RTRL-LMS-and-linear-regression

공식 코드

Prediction of the Position of External Markers Using a Recurrent Neural Network Trained With Unbiased Online Recurrent Optimization for Safe Lung Cancer Radiotherapy

https://github.com/prakashchhipa/magnification-prior-self-supervised-method

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Magnification Prior: A Self-Supervised Method for Learning Representations on Breast Cancer Histopathological Images

https://github.com/pranavphoenix/Backbones

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Which Backbone to Use: A Resource-efficient Domain Specific Comparison for Computer Vision

https://github.com/riblidezso/frcnn_cad

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Detecting and classifying lesions in mammograms with Deep Learning

https://github.com/richardobi/medigan

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High-resolution synthesis of high-density breast mammograms: Application to improved fairness in deep learning based mass detection

https://github.com/saniaesk/Breast-Cancer-Classification

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Evaluating Deep Learning Models for Breast Cancer Classification: A Comparative Study

https://github.com/simonHad/breast-cancer-detection-using-convolutional-neural-networks

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Breast Cancer Detection Using Convolutional Neural Networks

https://github.com/skrantidatta/Attention-based-Skin-Cancer-Classification

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Soft-Attention Improves Skin Cancer Classification Performance

https://github.com/sot176/longitudinal_mammogram_alignment

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Reconsidering Explicit Longitudinal Mammography Alignment for Enhanced Breast Cancer Risk Prediction

https://github.com/tbdair/fedperlv1.0

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Semi-Supervised Federated Peer Learning for Skin Lesion Classification

https://github.com/tkalbl/revisitingskintonefairness

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Revisiting Skin Tone Fairness in Dermatological Lesion Classification

https://github.com/tom1193/time-distance-transformer

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Time-distance vision transformers in lung cancer diagnosis from longitudinal computed tomography

https://github.com/tpnam0901/QTSeg

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QTSeg: A Query Token-Based Architecture for Efficient 2D Medical Image Segmentation

https://github.com/yhygao/Efficient_Data_Augmentation

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Enabling Data Diversity: Efficient Automatic Augmentation via Regularized Adversarial Training

https://github.com/yujialin-523/bcdnet

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BCDNet: A Fast Residual Neural Network For Invasive Ductal Carcinoma Detection

https://github.com/zakariasenousy/mcua-model

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MCUa: Multi-level Context and Uncertainty aware Dynamic Deep Ensemble for Breast Cancer Histology Image Classification

https://github.com/zhemingzuo/CFS

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Curvature-based Feature Selection with Application in Classifying Electronic Health Records

https://github.com/BITCS-Information-Retrieval-2020/search-rattailcollagen1

Encoding Visual Attributes in Capsules for Explainable Medical Diagnoses

https://github.com/CristianLazoQuispe/skin-lesion-segmentation-using-pix2pix

Skin Lesion Synthesis with Generative Adversarial Networks

https://github.com/EdoardoCicero/Neural-Networks-Project

Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening

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