lujiaxuan0520/CVPR-24-Papers-on-Pathology

Collection of CVPR papers on pathology image analysis tasks

CVPR-Papers-on-Pathology

Collection of CVPR papers on pathology image analysis tasks

Topics

• Foundation Model
• Multi-modalities: Visual-Language
• Interpretability
• Segmentation
• Representation learning
• Graph
• Reinforcement learning

Foundation Model

Feature Re-Embedding: Towards Foundation Model-Level Performance in Computational Pathology

• paper: https://arxiv.org/abs/2402.17228

• codes: https://github.com/dearcaat/rrt-mil

Prompting Vision Foundation Models for Pathology Image Analysis

• paper: tbd

• codes: tbd

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

• homepage: https://quilt-llava.github.io/

• paper: https://arxiv.org/abs/2312.04746

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

Multi-modalities: Visual-Language

Decomposing Disease Descriptions for Enhanced Pathology Detection: A Multi-Aspect Vision-Language Pre-training Framework

• paper: https://arxiv.org/abs/2403.07636

• codes: https://github.com/HieuPhan33/MAVL

CPLIP: Zero-Shot Learning for Histopathology with Comprehensive Vision-Language Alignment

• homepage: https://cplip.github.io/
• paper: tbd
• codes: https://github.com/iyyakuttiiyappan/CPLIP

ViLa-MIL: Dual-scale Vision-Language Multiple Instance Learning for Whole Slide Image Classification

• paper: tbd
• codes: tbd

Transcriptomics-guided Slide Representation Learning in Computational Pathology

• paper: tbd

• codes: tbd

Generalizable Whole Slide Image Classification with Fine-Grained Visual-Semantic Interaction

• paper: https://arxiv.org/html/2402.19326v1

• codes: tbd

Interpretability

SI-MIL: Taming Deep MIL for Self-Interpretability in Gigapixel Histopathology

• paper: https://arxiv.org/abs/2312.15010

• codes: tbd

Segmentation

PrPSeg: Universal Proposition Learning for Panoramic Renal Pathology Segmentation

• paper: https://arxiv.org/abs/2402.19286

• codes: tbd

Representation learning

Rotation-Agnostic Image Representation Learning for Digital Pathology

• paper: https://arxiv.org/abs/2311.08359

• codes: https://github.com/KimiaLabMayo/PathDino

Morphological Prototyping for Unsupervised Slide Representation Learning in Computational Pathology

• paper: tbd

• codes: tbd

ChAda-ViT: Channel Adaptive Attention for Joint Representation Learning of Heterogeneous Microscopy Images

• paper: https://link.zhihu.com/?target=https%3A//arxiv.org/abs/2311.15264

• codes: https://link.zhihu.com/?target=https%3A//github.com/nicoboou/chada_vit

Graph

Dynamic Graph Representation with Knowledge-aware Attention for Histopathology Whole Slide Image Analysis

• paper: https://arxiv.org/abs/2403.07719

• codes: https://github.com/WonderLandxD/WiKG

Reinforcement learning

Dynamic Policy-Driven Adaptive Multi-Instance Learning for Whole Slide Image Classification

• paper: https://arxiv.org/abs/2403.07939

• codes: https://github.com/titizheng/PAMIL