b'Faisal'

research

∙ 08/29/2023

A General-Purpose Self-Supervised Model for Computational Pathology

Tissue phenotyping is a fundamental computational pathology (CPath) task...

0 Richard J. Chen, et al. ∙

research

∙ 07/27/2023

Weakly Supervised AI for Efficient Analysis of 3D Pathology Samples

Human tissue and its constituent cells form a microenvironment that is f...

0 Andrew H. Song, et al. ∙

research

∙ 07/24/2023

Towards a Visual-Language Foundation Model for Computational Pathology

The accelerated adoption of digital pathology and advances in deep learn...

0 Ming Y. Lu, et al. ∙

research

∙ 06/13/2023

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

Contrastive visual language pretraining has emerged as a powerful method...

0 Ming Y. Lu, et al. ∙

research

∙ 04/13/2023

Modeling Dense Multimodal Interactions Between Biological Pathways and Histology for Survival Prediction

Integrating whole-slide images (WSIs) and bulk transcriptomics for predi...

0 Guillaume Jaume, et al. ∙

research

∙ 02/23/2023

Quantifying Modeling Feature Interactions: An Information Decomposition Framework

The recent explosion of interest in multimodal applications has resulted...

0 Paul Pu Liang, et al. ∙

research

∙ 10/31/2022

Embedding Space Augmentation for Weakly Supervised Learning in Whole-Slide Images

Multiple Instance Learning (MIL) is a widely employed framework for lear...

0 Imaad Zaffar, et al. ∙

research

∙ 06/17/2022

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

Supervised learning tasks such as cancer survival prediction from gigapi...

0 Iain Carmichael, et al. ∙

research

∙ 06/06/2022

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

Vision Transformers (ViTs) and their multi-scale and hierarchical variat...

22 Richard J. Chen, et al. ∙

research

∙ 10/01/2021

Algorithm Fairness in AI for Medicine and Healthcare

In the current development and deployment of many artificial intelligenc...

0 Richard J. Chen, et al. ∙

research

∙ 08/04/2021

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

The rapidly emerging field of deep learning-based computational patholog...

17 Richard J. Chen, et al. ∙

research

∙ 07/28/2021

Fast and Scalable Image Search For Histology

The expanding adoption of digital pathology has enabled the curation of ...

75 Chengkuan Chen, et al. ∙

research

∙ 07/27/2021

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 ...

16 Richard J. Chen, et al. ∙

research

∙ 07/25/2021

Deep Learning-based Frozen Section to FFPE Translation

Frozen sectioning (FS) is the preparation method of choice for microscop...

19 Kutsev Bengisu Ozyoruk, et al. ∙

research

∙ 09/21/2020

Federated Learning for Computational Pathology on Gigapixel Whole Slide Images

Deep Learning-based computational pathology algorithms have demonstrated...

29 Ming Y. Lu, et al. ∙

research

∙ 06/24/2020

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

Cancers of unknown primary (CUP), represent 1-3 enigmatic disease where ...

0 Ming Y. Lu, et al. ∙

research

∙ 04/20/2020

Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images

The rapidly emerging field of computational pathology has the potential ...

19 Ming Y. Lu, et al. ∙

research

∙ 12/18/2019

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

Cancer diagnosis, prognosis and therapeutic response predictions are bas...

18 Richard J. Chen, et al. ∙

research

∙ 10/29/2019

Weakly Supervised Prostate TMA Classification via Graph Convolutional Networks

Histology-based grade classification is clinically important for many ca...

28 Jingwen Wang, et al. ∙

research

∙ 10/23/2019

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

Convolutional neural networks can be trained to perform histology slide ...

20 Ming Y. Lu, et al. ∙

research

∙ 06/29/2019

SLAM Endoscopy enhanced by adversarial depth prediction

Medical endoscopy remains a challenging application for simultaneous loc...

1 Richard J. Chen, et al. ∙

research

∙ 06/12/2019

GANPOP: Generative Adversarial Network Prediction of Optical Properties from Single Snapshot Wide-field Images

We present a deep learning framework for wide-field, content-aware estim...

0 Mason T. Chen, et al. ∙

research

∙ 04/30/2019

Structured Prediction using cGANs with Fusion Discriminator

We propose the fusion discriminator, a single unified framework for inco...

10 Faisal, et al. ∙

research

∙ 11/18/2018

Multimodal Densenet

Humans make accurate decisions by interpreting complex data from multipl...

8 Faisal, et al. ∙

research

∙ 10/23/2018

DeepLSR: Deep learning approach for laser speckle reduction

We present a deep learning approach for laser speckle reduction ('DeepLS...

6 Taylor L. Bobrow, et al. ∙

research

∙ 09/29/2018

Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images

Nuclei segmentation is a fundamental task that is critical for various c...

0 Faisal, et al. ∙

research

∙ 08/22/2018

Rethinking Monocular Depth Estimation with Adversarial Training

Monocular depth estimation is an extensively studied computer vision pro...

10 Richard Chen, et al. ∙

research

∙ 05/22/2018

Deep Learning with Cinematic Rendering - Fine-Tuning Deep Neural Networks Using Photorealistic Medical Images

Deep learning has emerged as a powerful artificial intelligence tool to ...

0 Faisal, et al. ∙

research

∙ 11/17/2017

Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training

To realize the full potential of deep learning for medical imaging, larg...

0 Faisal, et al. ∙

research

∙ 10/30/2017

Deep Learning and Conditional Random Fields-based Depth Estimation and Topographical Reconstruction from Conventional Endoscopy

Colorectal cancer is the fourth leading cause of cancer deaths worldwide...

0 Faisal, et al. ∙

research

∙ 10/04/2016

Adaptive Graph-based Total Variation for Tomographic Reconstructions

Sparsity exploiting image reconstruction (SER) methods have been extensi...

0 Faisal, et al. ∙

research

∙ 03/16/2016

2D Discrete Fourier Transform with Simultaneous Edge Artifact Removal for Real-Time Applications

Two-Dimensional (2D) Discrete Fourier Transform (DFT) is a basic and com...

0 Faisal, et al. ∙

research

∙ 03/14/2016

Graph Based Sinogram Denoising for Tomographic Reconstructions

Limited data and low dose constraints are common problems in a variety o...

0 Faisal, et al. ∙

Faisal

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