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Found 12 papers, 7 papers with code
PathBench: A comprehensive comparison benchmark for pathology foundation models towards precision oncology
Currently, our evaluation of 19 PFMs shows that Virchow2 and H-Optimus-1 are the most effective models overall.
PathOrchestra: A Comprehensive Foundation Model for Computational Pathology with Over 100 Diverse Clinical-Grade Tasks
Overall, PathOrchestra exemplifies the feasibility and efficacy of a large-scale, self-supervised pathology foundation model, validated across a broad range of clinical-grade tasks.
FreeTumor: Large-Scale Generative Tumor Synthesis in Computed Tomography Images for Improving Tumor Recognition
To tackle this challenge, we introduce FreeTumor, an innovative Generative AI (GAI) framework to enable large-scale tumor synthesis for mitigating data scarcity.
Generalizable Cervical Cancer Screening via Large-scale Pretraining and Test-Time Adaptation
In retrospective cohorts, Smart-CCS achieved an overall area under the curve (AUC) value of 0. 965 and sensitivity of 0. 913 for cancer screening on 11 internal test datasets.
Context Matters: Query-aware Dynamic Long Sequence Modeling of Gigapixel Images
Whole slide image (WSI) analysis presents significant computational challenges due to the massive number of patches in gigapixel images.
FOCUS: Knowledge-enhanced Adaptive Visual Compression for Few-shot Whole Slide Image Classification
To this end, we introduce the knowledge-enhanced adaptive visual compression framework, dubbed FOCUS, which uniquely combines pathology FMs with language prior knowledge to enable a focused analysis of diagnostically relevant regions by prioritizing discriminative WSI patches.
Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation
Foundation models pretrained on large-scale datasets are revolutionizing the field of computational pathology (CPath).
A Multimodal Knowledge-enhanced Whole-slide Pathology Foundation Model
In this study, for the first time, we develop a pathology foundation model incorporating three levels of modalities: pathology slides, pathology reports, and gene expression data, which resulted in 26, 169 slide-level modality pairs from 10, 275 patients across 32 cancer types, amounting to over 116 million pathological patch images.
HistGen: Histopathology Report Generation via Local-Global Feature Encoding and Cross-modal Context Interaction
Histopathology serves as the gold standard in cancer diagnosis, with clinical reports being vital in interpreting and understanding this process, guiding cancer treatment and patient care.
LYSTO: The Lymphocyte Assessment Hackathon and Benchmark Dataset
Additionally, we present post-competition results where we show how the presented methods perform on an independent set of lung cancer slides, which was not part of the initial competition, as well as a comparison on lymphocyte assessment between presented methods and a panel of pathologists.
Reconstruct high-resolution multi-focal plane images from a single 2D wide field image
High-resolution 3D medical images are important for analysis and diagnosis, but axial scanning to acquire them is very time-consuming.
FFusionCGAN: An end-to-end fusion method for few-focus images using conditional GAN in cytopathological digital slides
However, although existing image fusion techniques, including traditional algorithms and deep learning-based algorithms, can generate high-quality fused images, they need multiple images with different focus depths in the same field of view.
