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whole slide images

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

https://github.com/yuqizhang-buaa/mamba2mil

공식 코드

Mamba2MIL: State Space Duality Based Multiple Instance Learning for Computational Pathology

https://github.com/zacharywang-007/g-hanet

공식 코드

Histo-Genomic Knowledge Distillation For Cancer Prognosis From Histopathology Whole Slide Images

https://github.com/zhanghx-iim-ahu/circleformer

공식 코드

CircleFormer: Circular Nuclei Detection in Whole Slide Images with Circle Queries and Attention

https://github.com/zhanglab2021/cpia_dataset

공식 코드

CPIA Dataset: A Comprehensive Pathological Image Analysis Dataset for Self-supervised Learning Pre-training

https://github.com/zhenqi-he/artifusion

공식 코드

Artifact Restoration in Histology Images with Diffusion Probabilistic Models

https://github.com/zhenrongshen/cellgan

공식 코드

CellGAN: Conditional Cervical Cell Synthesis for Augmenting Cytopathological Image Classification

https://github.com/zju-vipa/EVS-Net

공식 코드

Edge-competing Pathological Liver Vessel Segmentation with Limited Labels

https://github.com/zylbuaa/pibd

공식 코드

Prototypical Information Bottlenecking and Disentangling for Multimodal Cancer Survival Prediction

https://github.com/zzbuzzard/paths

공식 코드

PATHS: A Hierarchical Transformer for Efficient Whole Slide Image Analysis

https://gitlab.com/BioAI/RCC_DS

공식 코드

Renal Cell Carcinoma Detection and Subtyping with Minimal Point-Based Annotation in Whole-Slide Images

https://github.com/2023-MindSpore-1/ms-code-216/tree/main/rcnn

RCNN for Region of Interest Detection in Whole Slide Images

https://github.com/3dimaging/DeepLearningCamelyon

Deep Learning for Identifying Metastatic Breast Cancer

https://github.com/CODAIT/deep-histopath

A Unified Framework for Tumor Proliferation Score Prediction in Breast Histopathology

https://github.com/ChristianMarzahl/ObjectDetection

Deep Learning-Based Quantification of Pulmonary Hemosiderophages in Cytology Slides

https://github.com/DIDSR/DeepLearningCamelyon_II

Deep Learning for Identifying Metastatic Breast Cancer

https://github.com/IPMI-ICNS-UKE/multiscale

Multi-scale fully convolutional neural networks for histopathology image segmentation: from nuclear aberrations to the global tissue architecture

https://github.com/JJ-ZHOU-Code/DAMM-Diffusion

Multimodal Optimal Transport-based Co-Attention Transformer with Global Structure Consistency for Survival Prediction

https://github.com/JarveeLee/HybridSupervisionLearning_Pathology

Hybrid Supervision Learning for Pathology Whole Slide Image Classification

https://github.com/MarvinLer/SparseConvMIL

SparseConvMIL: Sparse Convolutional Context-Aware Multiple Instance Learning for Whole Slide Image Classification

https://github.com/MichelML/ml-cellsignal

Automated Classification of Histopathology Images Using Transfer Learning

https://github.com/MindCode-4/code-7/tree/main/falformer

FALFormer: Feature-aware Landmarks self-attention for Whole-slide Image Classification

https://github.com/MindSpore-scientific-2/code-12/tree/main/falformer

FALFormer: Feature-aware Landmarks self-attention for Whole-slide Image Classification

https://github.com/Mr-TalhaIlyas/Prerpcessing-PanNuke-Nuclei-Instance-Segmentation-Dataset

PanNuke Dataset Extension, Insights and Baselines

https://github.com/NadiaBlostein/Ai4GoodLab_CombatCervicalCancer

CNN-based Approach for Cervical Cancer Classification in Whole-Slide Histopathology Images

https://github.com/Noodles-321/OralScreen

A Deep Learning based Pipeline for Efficient Oral Cancer Screening on Whole Slide Images

https://github.com/SINTEFMedtek/FAST-Pathology

Hybrid guiding: A multi-resolution refinement approach for semantic segmentation of gigapixel histopathological images

https://github.com/Sdhir/deepCIN

Feature based Sequential Classifier with Attention Mechanism

https://github.com/TIO-IKIM/medical_image_deidentification

De-Identification of Medical Imaging Data: A Comprehensive Tool for Ensuring Patient Privacy

https://github.com/TheSaintIndiano/Bamboo-Forest-Detection

Deep Learning for Identifying Metastatic Breast Cancer

https://github.com/aaparna/UNet-Image-Segmentation

PanNuke Dataset Extension, Insights and Baselines

https://github.com/aldraus/quilt-llava

Quilt-LLaVA: Visual Instruction Tuning by Extracting Localized Narratives from Open-Source Histopathology Videos

https://github.com/alexjfoote/reetoolbox

PanNuke Dataset Extension, Insights and Baselines

https://github.com/anastasiia-kornilova/MMDF

FFusionCGAN: An end-to-end fusion method for few-focus images using conditional GAN in cytopathological digital slides

https://github.com/birkhoffkiki/gpfm

Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation

https://github.com/bugs-creator/latentartifusion

Artifact Restoration in Histology Images with Diffusion Probabilistic Models

https://github.com/dazhangyu123/acmil

AEM: Attention Entropy Maximization for Multiple Instance Learning based Whole Slide Image Classification

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