Search Results for author: Drew F. K. Williamson

Found 12 papers, 10 papers with code

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

1 code implementation17 Jun 2022 Iain Carmichael, Andrew H. Song, Richard J. Chen, Drew F. K. Williamson, Tiffany Y. Chen, Faisal Mahmood

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.

Survival Prediction whole slide images

Differentiable Zooming for Multiple Instance Learning on Whole-Slide Images

no code implementations26 Apr 2022 Kevin Thandiackal, Boqi Chen, Pushpak Pati, Guillaume Jaume, Drew F. K. Williamson, Maria Gabrani, Orcun Goksel

Multiple Instance Learning (MIL) methods have become increasingly popular for classifying giga-pixel sized Whole-Slide Images (WSIs) in digital pathology.

Multiple Instance Learning whole slide images

Algorithm Fairness in AI for Medicine and Healthcare

no code implementations1 Oct 2021 Richard J. Chen, Tiffany Y. Chen, Jana Lipkova, Judy J. Wang, Drew F. K. Williamson, Ming Y. Lu, Sharifa Sahai, Faisal Mahmood

In the current development and deployment of many artificial intelligence (AI) systems in healthcare, algorithm fairness is a challenging problem in delivering equitable care.

Disentanglement Fairness +1

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

1 code implementation4 Aug 2021 Richard J. Chen, Ming Y. Lu, Drew F. K. Williamson, Tiffany Y. Chen, Jana Lipkova, Muhammad Shaban, Maha Shady, Mane Williams, Bumjin Joo, Zahra Noor, Faisal Mahmood

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.

Multimodal Deep Learning whole slide images

Fast and Scalable Image Search For Histology

1 code implementation28 Jul 2021 Chengkuan Chen, Ming Y. Lu, Drew F. K. Williamson, Tiffany Y. Chen, Andrew J. Schaumberg, Faisal Mahmood

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.

Image Retrieval Retrieval +1

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

1 code implementation27 Jul 2021 Richard J. Chen, Ming Y. Lu, Muhammad Shaban, Chengkuan Chen, Tiffany Y. Chen, Drew F. K. Williamson, Faisal Mahmood

Cancer prognostication is a challenging task in computational pathology that requires context-aware representations of histology features to adequately infer patient survival.

Survival Prediction whole slide images

Deep Learning-based Frozen Section to FFPE Translation

1 code implementation25 Jul 2021 Kutsev Bengisu Ozyoruk, Sermet Can, Guliz Irem Gokceler, Kayhan Basak, Derya Demir, Gurdeniz Serin, Uguray Payam Hacisalihoglu, Emirhan Kurtuluş, Berkan Darbaz, Ming Y. Lu, Tiffany Y. Chen, Drew F. K. Williamson, Funda Yilmaz, Faisal Mahmood, Mehmet Turan

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.

Decision Making Translation +1

Federated Learning for Computational Pathology on Gigapixel Whole Slide Images

1 code implementation21 Sep 2020 Ming Y. Lu, Dehan Kong, Jana Lipkova, Richard J. Chen, Rajendra Singh, Drew F. K. Williamson, Tiffany Y. Chen, Faisal Mahmood

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.

Federated Learning Multiple Instance Learning +3

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

1 code implementation24 Jun 2020 Ming Y. Lu, Melissa Zhao, Maha Shady, Jana Lipkova, Tiffany Y. Chen, Drew F. K. Williamson, Faisal Mahmood

Cancer of unknown primary (CUP) is an enigmatic group of diagnoses where the primary anatomical site of tumor origin cannot be determined.

whole slide images

Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images

1 code implementation20 Apr 2020 Ming Y. Lu, Drew F. K. Williamson, Tiffany Y. Chen, Richard J. Chen, Matteo Barbieri, Faisal Mahmood

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.

Domain Adaptation Multiple Instance Learning +2

Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis

1 code implementation18 Dec 2019 Richard J. Chen, Ming Y. Lu, Jingwen Wang, Drew F. K. Williamson, Scott J. Rodig, Neal I. Lindeman, Faisal Mahmood

Cancer diagnosis, prognosis, and therapeutic response predictions are based on morphological information from histology slides and molecular profiles from genomic data.

Feature Importance

A flat persistence diagram for improved visualization of persistent homology

1 code implementation11 Dec 2018 Raoul R. Wadhwa, Andrew Dhawan, Drew F. K. Williamson, Jacob G. Scott

Visualization in the emerging field of topological data analysis has progressed from persistence barcodes and persistence diagrams to display of two-parameter persistent homology.

Applications

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