Explanipedia

The FELIX Project: Deep Networks To Detect Pancreatic Neoplasms Open

Yingda Xia, Qihang Yu, Linda C. Chu, Satomi Kawamoto, Seyoun Park , et al. · 2022

Tens of millions of abdominal images are obtained with computed tomography (CT) in the U.S. each year but pancreatic cancers are sometimes not initially detected in these images. We here describe a suite of algorithms (named FELIX) that ca…

Editorial: Advances in AI methods for computational pathology Open

Qi Xin, Jun Kong, Fuyong Xing, Guotai Wang, Zhuotun Zhu · 2022

EDITORIAL article Front. Med., 21 September 2022Sec. Pathology https://doi.org/10.3389/fmed.2022.974857

Comprehensive and Clinically Accurate Head and Neck Organs at Risk Delineation via Stratified Deep Learning: A Large-scale Multi-Institutional Study Open

Dazhou Guo, Jia Ge, Xianghua Ye, Senxiang Yan, Xin Yi , et al. · 2021

Accurate organ at risk (OAR) segmentation is critical to reduce the radiotherapy post-treatment complications. Consensus guidelines recommend a set of more than 40 OARs in the head and neck (H&N) region, however, due to the predictable pro…

Lymph Node Gross Tumor Volume Detection in Oncology Imaging via Relationship Learning Using Graph Neural Network Open

Chun-Hung Chao, Zhuotun Zhu, Dazhou Guo, Ke Yan, Tsung‐Ying Ho , et al. · 2020

Determining the spread of GTV$_{LN}$ is essential in defining the respective resection or irradiating regions for the downstream workflows of surgical resection and radiotherapy for many cancers. Different from the more common enlarged lym…

Lymph Node Gross Tumor Volume Detection and Segmentation via Distance-based Gating using 3D CT/PET Imaging in Radiotherapy Open

Zhuotun Zhu, Dakai Jin, Ke Yan, Tsung‐Ying Ho, Xianghua Ye , et al. · 2020

Finding, identifying and segmenting suspicious cancer metastasized lymph nodes from 3D multi-modality imaging is a clinical task of paramount importance. In radiotherapy, they are referred to as Lymph Node Gross Tumor Volume (GTVLN). Deter…

Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation Open

Yingda Xia, Dong Yang, Zhiding Yu, Fengze Liu, Jinzheng Cai , et al. · 2020

Detecting Scatteredly-Distributed, Small, andCritically Important Objects in 3D OncologyImaging via Decision Stratification Open

Zhuotun Zhu, Ke Yan, Dakai Jin, Jinzheng Cai, Tsung‐Ying Ho , et al. · 2020

Finding and identifying scatteredly-distributed, small, and critically important objects in 3D oncology images is very challenging. We focus on the detection and segmentation of oncology-significant (or suspicious cancer metastasized) lymp…

Organ at Risk Segmentation for Head and Neck Cancer using Stratified Learning and Neural Architecture Search Open

Dazhou Guo, Dakai Jin, Zhuotun Zhu, Tsung‐Ying Ho, Adam P. Harrison , et al. · 2020

OAR segmentation is a critical step in radiotherapy of head and neck (H&N) cancer, where inconsistencies across radiation oncologists and prohibitive labor costs motivate automated approaches. However, leading methods using standard fully …

Segmentation for Classification of Screening Pancreatic Neuroendocrine Tumors Open

Zhuotun Zhu, Yongyi Lu, Wei Shen, Elliot K. Fishman, Alan Yuille · 2020

This work presents comprehensive results to detect in the early stage the pancreatic neuroendocrine tumors (PNETs), a group of endocrine tumors arising in the pancreas, which are the second common type of pancreatic cancer, by checking the…

V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation Open

Zhuotun Zhu, Chenxi Liu, Dong Yang, Alan Yuille, Daguang Xu · 2019

Deep learning algorithms, in particular 2D and 3D fully convolutional neural networks (FCNs), have rapidly become the mainstream methodology for volumetric medical image segmentation. However, 2D convolutions cannot fully leverage the rich…

3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training Open

Yingda Xia, Fengze Liu, Dong Yang, Jinzheng Cai, Lequan Yu , et al. · 2018

While making a tremendous impact in various fields, deep neural networks usually require large amounts of labeled data for training which are expensive to collect in many applications, especially in the medical domain. Unlabeled data, on t…

Multi-Scale Coarse-to-Fine Segmentation for Screening Pancreatic Ductal Adenocarcinoma Open

Zhuotun Zhu, Yingda Xia, Lingxi Xie, Elliot K. Fishman, Alan Yuille · 2018

We propose an intuitive approach of detecting pancreatic ductal adenocarcinoma (PDAC), the most common type of pancreatic cancer, by checking abdominal CT scans. Our idea is named multi-scale segmentation-for-classification, which classifi…

Bridging the Gap Between 2D and 3D Organ Segmentation. Open

Yingda Xia, Lingxi Xie, Fengze Liu, Zhuotun Zhu, Elliot K. Fishman , et al. · 2018

There has been a debate on whether to use 2D or 3D deep neural networks for volumetric organ segmentation. Both 2D and 3D models have their advantages and disadvantages. In this paper, we present an alternative framework, which trains 2D n…

Bridging the Gap Between 2D and 3D Organ Segmentation with Volumetric Fusion Net Open

Yingda Xia, Lingxi Xie, Fengze Liu, Zhuotun Zhu, Elliot K. Fishman , et al. · 2018

There has been a debate on whether to use 2D or 3D deep neural networks for volumetric organ segmentation. Both 2D and 3D models have their advantages and disadvantages. In this paper, we present an alternative framework, which trains 2D n…

A 3D Coarse-to-Fine Framework for Volumetric Medical Image Segmentation Open

Zhuotun Zhu, Yingda Xia, Wei Shen, Elliot K. Fishman, Alan Yuille · 2017

In this paper, we adopt 3D Convolutional Neural Networks to segment volumetric medical images. Although deep neural networks have been proven to be very effective on many 2D vision tasks, it is still challenging to apply them to 3D tasks d…

A 3D Coarse-to-Fine Framework for Automatic Pancreas Segmentation. Open

Zhuotun Zhu, Yingda Xia, Wei Shen, Elliot K. Fishman, Alan Yuille · 2017

Object Recognition with and without Objects Open

Zhuotun Zhu, Lingxi Xie, Alan Yuille · 2017

While recent deep neural networks have achieved a promising performance on object recognition, they rely implicitly on the visual contents of the whole image. In this paper, we train deep neural networks on the foreground (object) and back…

Object Recognition with and without Objects Open

Zhuotun Zhu, Lingxi Xie, Alan Yuille · 2016

While recent deep neural networks have achieved a promising performance on object recognition, they rely implicitly on the visual contents of the whole image. In this paper, we train deep neural net- works on the foreground (object) and ba…

Bag Reference Vector for Multi-instance Learning Open

Hanqiang Song, Zhuotun Zhu, Xinggang Wang · 2015

Multi-instance learning (MIL) has a wide range of applications due to its distinctive characteristics. Although many state-of-the-art algorithms have achieved decent performances, a plurality of existing methods solve the problem only in i…

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