Search Results for author: Tong Ding

Found 5 papers, 1 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

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

IUP: An Intelligent Utility Prediction Scheme for Solid-State Fermentation in 5G IoT

no code implementations28 Mar 2021 Min Wang, Shanchen Pang, Tong Ding, Sibo Qiao, Xue Zhai, Shuo Wang, Neal N. Xiong, Zhengwen Huang

In addition, we design a utility prediction model for SSF based on the Generative Adversarial Networks (GAN) and Fully Connected Neural Network (FCNN).

Few-Shot Learning

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