Search Results for author: Bing Ren

Found 9 papers, 3 papers with code

A Petri Dish for Histopathology Image Analysis

no code implementations29 Jan 2021 Jerry Wei, Arief Suriawinata, Bing Ren, Xiaoying Liu, Mikhail Lisovsky, Louis Vaickus, Charles Brown, Michael Baker, Naofumi Tomita, Lorenzo Torresani, Jason Wei, Saeed Hassanpour

With the rise of deep learning, there has been increased interest in using neural networks for histopathology image analysis, a field that investigates the properties of biopsy or resected specimens traditionally manually examined under a microscope by pathologists.

Transfer Learning

Development and Evaluation of a Deep Neural Network for Histologic Classification of Renal Cell Carcinoma on Biopsy and Surgical Resection Slides

no code implementations30 Oct 2020 Mengdan Zhu, Bing Ren, Ryland Richards, Matthew Suriawinata, Naofumi Tomita, Saeed Hassanpour

In this study, we developed a deep neural network model that can accurately classify digitized surgical resection slides and biopsy slides into five related classes: clear cell RCC, papillary RCC, chromophobe RCC, renal oncocytoma, and normal.

Classification General Classification

Learn like a Pathologist: Curriculum Learning by Annotator Agreement for Histopathology Image Classification

no code implementations29 Sep 2020 Jerry Wei, Arief Suriawinata, Bing Ren, Xiaoying Liu, Mikhail Lisovsky, Louis Vaickus, Charles Brown, Michael Baker, Mustafa Nasir-Moin, Naofumi Tomita, Lorenzo Torresani, Jason Wei, Saeed Hassanpour

Based on the nature of histopathology images, a range of difficulty inherently exists among examples, and, since medical datasets are often labeled by multiple annotators, annotator agreement can be used as a natural proxy for the difficulty of a given example.

General Classification Image Classification

Difficulty Translation in Histopathology Images

1 code implementation27 Apr 2020 Jerry Wei, Arief Suriawinata, Xiaoying Liu, Bing Ren, Mustafa Nasir-Moin, Naofumi Tomita, Jason Wei, Saeed Hassanpour

Our model comprises a scorer, which provides an output confidence to measure the difficulty of images, and an image translator, which learns to translate images from easy-to-classify to hard-to-classify using a training set defined by the scorer.

Translation

Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinator

no code implementations22 Nov 2019 Shengwen Yang, Bing Ren, Xuhui Zhou, Li-Ping Liu

The system is built on the pa-rameter server architecture and aims to speed up the model training via utilizing a cluster of servers in case of large volume of training data.

Federated Learning Transfer Learning

Generative Image Translation for Data Augmentation in Colorectal Histopathology Images

1 code implementation13 Oct 2019 Jerry Wei, Arief Suriawinata, Louis Vaickus, Bing Ren, Xiaoying Liu, Jason Wei, Saeed Hassanpour

We present an image translation approach to generate augmented data for mitigating data imbalances in a dataset of histopathology images of colorectal polyps, adenomatous tumors that can lead to colorectal cancer if left untreated.

Data Augmentation Image Classification +1

Attention-Based Deep Neural Networks for Detection of Cancerous and Precancerous Esophagus Tissue on Histopathological Slides

1 code implementation20 Nov 2018 Naofumi Tomita, Behnaz Abdollahi, Jason Wei, Bing Ren, Arief Suriawinata, Saeed Hassanpour

Deep learning-based methods, such as the sliding window approach for cropped-image classification and heuristic aggregation for whole-slide inference, for analyzing histological patterns in high-resolution microscopy images have shown promising results.

Crop Classification General Classification +2

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