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Found 22 papers, 17 papers with code
Multimodal Whole Slide Foundation Model for Pathology
The field of computational pathology has been transformed with recent advances in foundation models that encode histopathology region-of-interests (ROIs) into versatile and transferable feature representations via self-supervised learning (SSL).
HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis
Spatial transcriptomics enables interrogating the molecular composition of tissue with ever-increasing resolution and sensitivity.
Transcriptomics-guided Slide Representation Learning in Computational Pathology
Across three independent test datasets consisting of 1, 265 breast WSIs, 1, 946 lung WSIs, and 4, 584 liver WSIs, Tangle shows significantly better few-shot performance compared to supervised and SSL baselines.
Morphological Prototyping for Unsupervised Slide Representation Learning in Computational Pathology
Representation learning of pathology whole-slide images (WSIs) has been has primarily relied on weak supervision with Multiple Instance Learning (MIL).
Artificial Intelligence for Digital and Computational Pathology
Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence, including deep learning, have boosted the field of computational pathology.
A Foundational Multimodal Vision Language AI Assistant for Human Pathology
We compare PathChat against several multimodal vision language AI assistants as well as GPT4V, which powers the commercially available multimodal general purpose AI assistant ChatGPT-4.
A General-Purpose Self-Supervised Model for Computational Pathology
Tissue phenotyping is a fundamental computational pathology (CPath) task in learning objective characterizations of histopathologic biomarkers in anatomic pathology.
Weakly Supervised AI for Efficient Analysis of 3D Pathology Samples
Human tissue and its constituent cells form a microenvironment that is fundamentally three-dimensional (3D).
Towards a Visual-Language Foundation Model for Computational Pathology
The accelerated adoption of digital pathology and advances in deep learning have enabled the development of powerful models for various pathology tasks across a diverse array of diseases and patient cohorts.
Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Images
In this paper we present MI-Zero, a simple and intuitive framework for unleashing the zero-shot transfer capabilities of contrastively aligned image and text models on gigapixel histopathology whole slide images, enabling multiple downstream diagnostic tasks to be carried out by pretrained encoders without requiring any additional labels.
Incorporating intratumoral heterogeneity into weakly-supervised deep learning models via variance pooling
Supervised learning tasks such as cancer survival prediction from gigapixel whole slide images (WSIs) are a critical challenge in computational pathology that requires modeling complex features of the tumor microenvironment.
Differentiable Zooming for Multiple Instance Learning on Whole-Slide Images
Multiple Instance Learning (MIL) methods have become increasingly popular for classifying giga-pixel sized Whole-Slide Images (WSIs) in digital pathology.
Algorithm Fairness in AI for Medicine and Healthcare
In the current development and deployment of many artificial intelligence (AI) systems in healthcare, algorithm fairness is a challenging problem in delivering equitable care.
Pan-Cancer Integrative Histology-Genomic Analysis via Interpretable Multimodal Deep Learning
To validate that these model explanations are prognostic, we further analyzed high attention morphological regions in WSIs, which indicates that tumor-infiltrating lymphocyte presence corroborates with favorable cancer prognosis on 9 out of 14 cancer types studied.
Fast and Scalable Image Search For Histology
Similar pathology image search offers the opportunity to comb through large historical repositories of gigapixel WSIs to identify cases with similar morphological features and can be particularly useful for diagnosing rare diseases, identifying similar cases for predicting prognosis, treatment outcomes, and potential clinical trial success.
Whole Slide Images are 2D Point Clouds: Context-Aware Survival Prediction using Patch-based Graph Convolutional Networks
Cancer prognostication is a challenging task in computational pathology that requires context-aware representations of histology features to adequately infer patient survival.
Deep Learning-based Frozen Section to FFPE Translation
In this paper, we propose an artificial intelligence (AI) method that improves FS image quality by computationally transforming frozen-sectioned whole-slide images (FS-WSIs) into whole-slide FFPE-style images in minutes.
Federated Learning for Computational Pathology on Gigapixel Whole Slide Images
In this paper, we introduce privacy-preserving federated learning for gigapixel whole slide images in computational pathology using weakly-supervised attention multiple instance learning and differential privacy.
Deep Learning-based Computational Pathology Predicts Origins for Cancers of Unknown Primary
Cancer of unknown primary (CUP) is an enigmatic group of diagnoses where the primary anatomical site of tumor origin cannot be determined.
Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images
CLAM is a general-purpose and adaptable method that can be used for a variety of different computational pathology tasks in both clinical and research settings.
Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis
Cancer diagnosis, prognosis, and therapeutic response predictions are based on morphological information from histology slides and molecular profiles from genomic data.
A flat persistence diagram for improved visualization of persistent homology
Visualization in the emerging field of topological data analysis has progressed from persistence barcodes and persistence diagrams to display of two-parameter persistent homology.
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