Search Results for author: Richard J. Chen

Found 15 papers, 11 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

Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning

1 code implementation CVPR 2022 Richard J. Chen, Chengkuan Chen, Yicong Li, Tiffany Y. Chen, Andrew D. Trister, Rahul G. Krishnan, Faisal Mahmood

Vision Transformers (ViTs) and their multi-scale and hierarchical variations have been successful at capturing image representations but their use has been generally studied for low-resolution images (e. g. - 256x256, 384384).

Self-Supervised Learning Survival Prediction

Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology

1 code implementation1 Mar 2022 Richard J. Chen, Rahul G. Krishnan

Tissue phenotyping is a fundamental task in learning objective characterizations of histopathologic biomarkers within the tumor-immune microenvironment in cancer pathology.

Knowledge Distillation Transfer Learning

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

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

Multimodal Co-Attention Transformer for Survival Prediction in Gigapixel Whole Slide Images

1 code implementation ICCV 2021 Richard J. Chen, Ming Y. Lu, Wei-Hung Weng, Tiffany Y. Chen, Drew F.K. Williamson, Trevor Manz, Maha Shady, Faisal Mahmood

Survival outcome prediction is a challenging weakly-supervised and ordinal regression task in computational pathology that involves modeling complex interactions within the tumor microenvironment in gigapixel whole slide images (WSIs).

Multiple Instance Learning Question Answering +5

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

VR-Caps: A Virtual Environment for Capsule Endoscopy

1 code implementation29 Aug 2020 Kagan Incetan, Ibrahim Omer Celik, Abdulhamid Obeid, Guliz Irem Gokceler, Kutsev Bengisu Ozyoruk, Yasin Almalioglu, Richard J. Chen, Faisal Mahmood, Hunter Gilbert, Nicholas J. Durr, Mehmet Turan

Current capsule endoscopes and next-generation robotic capsules for diagnosis and treatment of gastrointestinal diseases are complex cyber-physical platforms that must orchestrate complex software and hardware functions.

Depth Estimation Visual Localization

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

EndoL2H: Deep Super-Resolution for Capsule Endoscopy

2 code implementations13 Feb 2020 Yasin Almalioglu, Kutsev Bengisu Ozyoruk, Abdulkadir Gokce, Kagan Incetan, Guliz Irem Gokceler, Muhammed Ali Simsek, Kivanc Ararat, Richard J. Chen, Nicholas J. Durr, Faisal Mahmood, Mehmet Turan

Although wireless capsule endoscopy is the preferred modality for diagnosis and assessment of small bowel diseases, the poor camera resolution is a substantial limitation for both subjective and automated diagnostics.

Super-Resolution

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

Weakly Supervised Prostate TMA Classification via Graph Convolutional Networks

no code implementations29 Oct 2019 Jingwen Wang, Richard J. Chen, Ming Y. Lu, Alexander Baras, Faisal Mahmood

In prostate cancer, the Gleason score is a grading system used to measure the aggressiveness of prostate cancer from the spatial organization of cells and the distribution of glands.

Classification General Classification

SLAM Endoscopy enhanced by adversarial depth prediction

no code implementations29 Jun 2019 Richard J. Chen, Taylor L. Bobrow, Thomas Athey, Faisal Mahmood, Nicholas J. Durr

Medical endoscopy remains a challenging application for simultaneous localization and mapping (SLAM) due to the sparsity of image features and size constraints that prevent direct depth-sensing.

Depth Prediction Monocular Depth Estimation +1

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