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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/lts5/slip

공식 코드

Slide-Level Prompt Learning with Vision Language Models for Few-Shot Multiple Instance Learning in Histopathology

https://github.com/lucidrains/imagen-pytorch

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Ultra-Resolution Cascaded Diffusion Model for Gigapixel Image Synthesis in Histopathology

https://github.com/ma3oun/gbheterogeneity

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Uncovering the Genetic Basis of Glioblastoma Heterogeneity through Multimodal Analysis of Whole Slide Images and RNA Sequencing Data

https://github.com/magic-ai4med/kep

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Knowledge-enhanced Visual-Language Pretraining for Computational Pathology

https://github.com/mahmoodlab/CLAM

공식 코드

Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images

https://github.com/mahmoodlab/FISH

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Fast and Scalable Image Search For Histology

https://github.com/mahmoodlab/MMP

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Multimodal Prototyping for cancer survival prediction

https://github.com/mahmoodlab/PORPOISE

공식 코드

Pan-Cancer Integrative Histology-Genomic Analysis via Interpretable Multimodal Deep Learning

https://github.com/mahmoodlab/Patch-GCN

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Whole Slide Images are 2D Point Clouds: Context-Aware Survival Prediction using Patch-based Graph Convolutional Networks

https://github.com/mahmoodlab/TOAD

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Deep Learning-based Computational Pathology Predicts Origins for Cancers of Unknown Primary

https://github.com/mahmoodlab/VARPOOL

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Incorporating intratumoral heterogeneity into weakly-supervised deep learning models via variance pooling

https://github.com/mahmoodlab/hest

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HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis

https://github.com/mahmoodlab/madeleine

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Multistain Pretraining for Slide Representation Learning in Pathology

https://github.com/mahmoodlab/mi-zero

공식 코드

Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Images

https://github.com/mahmoodlab/panther

공식 코드

Morphological Prototyping for Unsupervised Slide Representation Learning in Computational Pathology

https://github.com/mahmoodlab/patho-bench

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Molecular-driven Foundation Model for Oncologic Pathology

https://github.com/mahmoodlab/survpath

공식 코드

Modeling Dense Multimodal Interactions Between Biological Pathways and Histology for Survival Prediction

https://github.com/mahmoodlab/tangle

공식 코드

Transcriptomics-guided Slide Representation Learning in Computational Pathology

https://github.com/mahmoodlab/trident

공식 코드

Molecular-driven Foundation Model for Oncologic Pathology

https://github.com/mahmoodlab/uni

공식 코드

Morphological Prototyping for Unsupervised Slide Representation Learning in Computational Pathology

https://github.com/masatsuneki/histopathology-image-caption

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Inference of captions from histopathological patches

https://github.com/masih4/CryoNuSeg

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CryoNuSeg: A Dataset for Nuclei Instance Segmentation of Cryosectioned H&E-Stained Histological Images

https://github.com/matouse/rot-invariance-analysis

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Are the Latent Representations of Foundation Models for Pathology Invariant to Rotation?

https://github.com/maubreville/MitosisRegionOfInterest

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Deep learning algorithms out-perform veterinary pathologists in detecting the mitotically most active tumor region

https://github.com/maxhallemeesch/praline

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Prior Knowledge Injection into Deep Learning Models Predicting Gene Expression from Whole Slide Images

https://github.com/miccaiif/dgmil

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DGMIL: Distribution Guided Multiple Instance Learning for Whole Slide Image Classification

https://github.com/mics-lab/s4_digital_pathology

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Structured State Space Models for Multiple Instance Learning in Digital Pathology

https://github.com/mlandcv/MultiPathGAN

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MultiPathGAN: Structure Preserving Stain Normalization using Unsupervised Multi-domain Adversarial Network with Perception Loss

https://github.com/mskcc-computational-pathology/dmmn-osteosarcoma

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Deep Learning-Based Objective and Reproducible Osteosarcoma Chemotherapy Response Assessment and Outcome Prediction

https://github.com/mskcc-computational-pathology/dmmn-ovary

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Deep Interactive Learning-based ovarian cancer segmentation of H&E-stained whole slide images to study morphological patterns of BRCA mutation

https://github.com/mwod/deeperhistreg

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DeeperHistReg: Robust Whole Slide Images Registration Framework

https://github.com/nabavilab/prostate-cancer

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Weakly-Supervised Deep Learning Model for Prostate Cancer Diagnosis and Gleason Grading of Histopathology Images

https://github.com/nadeemlab/deepliif

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Rethinking Histology Slide Digitization Workflows for Low-Resource Settings

https://github.com/neelkanwal/equipping-computational-pathology-systems-with-artifact-processing-pipeline

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Equipping Computational Pathology Systems with Artifact Processing Pipelines: A Showcase for Computation and Performance Trade-offs

https://github.com/nellaker-group/placenta

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A New Graph Node Classification Benchmark: Learning Structure from Histology Cell Graphs

https://github.com/nexgem/triplex

공식 코드

Accurate Spatial Gene Expression Prediction by integrating Multi-resolution features

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