Search Results for author: Ming Y. Lu

Found 17 papers, 10 papers with code

A Foundational Multimodal Vision Language AI Assistant for Human Pathology

no code implementations13 Dec 2023 Ming Y. Lu, Bowen Chen, Drew F. K. Williamson, Richard J. Chen, Kenji Ikamura, Georg Gerber, Ivy Liang, Long Phi Le, Tong Ding, Anil V Parwani, Faisal Mahmood

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.

Decision Making Language Modelling +2

Artificial Intelligence for Digital and Computational Pathology

no code implementations13 Dec 2023 Andrew H. Song, Guillaume Jaume, Drew F. K. Williamson, Ming Y. Lu, Anurag Vaidya, Tiffany R. Miller, Faisal Mahmood

Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence, including deep learning, have boosted the field of computational pathology.

whole slide images

Towards a Visual-Language Foundation Model for Computational Pathology

no code implementations24 Jul 2023 Ming Y. Lu, Bowen Chen, Drew F. K. Williamson, Richard J. Chen, Ivy Liang, Tong Ding, Guillaume Jaume, Igor Odintsov, Andrew Zhang, Long Phi Le, Georg Gerber, Anil V Parwani, Faisal Mahmood

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.

Contrastive Learning Image Classification +3

Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Images

1 code implementation CVPR 2023 Ming Y. Lu, Bowen Chen, Andrew Zhang, Drew F. K. Williamson, Richard J. Chen, Tong Ding, Long Phi Le, Yung-Sung Chuang, Faisal Mahmood

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.

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

2 code implementations28 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

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).

Attribute Multiple Instance Learning +6

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 +4

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.

Clustering Domain Adaptation +3

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

Semi-Supervised Histology Classification using Deep Multiple Instance Learning and Contrastive Predictive Coding

no code implementations23 Oct 2019 Ming Y. Lu, Richard J. Chen, Jingwen Wang, Debora Dillon, Faisal Mahmood

Convolutional neural networks can be trained to perform histology slide classification using weak annotations with multiple instance learning (MIL).

Binary Classification Classification +4

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