To advance the clinical diagnosis using cutting-edge machine learning techniques
Brief Bio
Now, I am a principal researcher in Medical AI at Shanghai Artificial Intelligence Laboratory. Previously, I briefly worked as a senior staff algorithm engineer at Alibaba DAMO Academy. Before moving back to China, I worked as a senior applied research scientist at Nvidia Corporation from 2018 to 2021 and a visiting research fellow in Dr. Ronald Summers's CAD lab at NIH Clinical Center from 2015 to 2018. From Dec. 2013 to Jun. 2015, I was a product manager of multi-modality post-processing workstations in Shanghai United Imaging Healthcare (UIH). Prior, I worked as an algorithm engineer and then dept. manager in Computer Aided Diagnosis (CAD) Department Healthcare Software BU of UIH. I received my PhD in Computer Science with the supervision of Prof. Majid Mirmehdi at University of Bristol, UK in 2011. My research interests include computer vision, vision and language, machine learning, deep learning and their applications in medical imaging.
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My latest CV -- Google Scholar -- ORCID Page
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Email： xiaosong.wang AT live.com or xiaosong.wang AT ieee.org
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Recruiting interns and researchers at all levels. Email me if you are interested.
ChestX-ray Dataset
- Dataset download link: NIH Clinical Center Box.
- [Sep.
26, 2017] First Release of the ChestX-ray dataset
- [Dec. 16, 2017] Release data split files: train_val_list.txt and test_list.txt
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Updates
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[Mar. 29rd, 2022] News: I start a new position as a PI at Shanghai AI Laboratory and we are recruiting researcher and algorithm engineers at all levels. - [Feb. 20th, 2018] News: Two of our papers are accepted in CVPR 2018, i.e. "TieNet: Text-Image Embedding Network for Common Thorax Disease Classification and Reporting in Chest X-rays." and "Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-scale Lesion Database."
- [Nov. 16th, 2017] News: We are selected to recieve a prestigious NIH Clincial Center CEO Award for advancing radiology science and patient care by organizing and releasing a large dataset of radiology images to the public.
- [Sep. 26th, 2017] News: the ChestX-ray14 dataset is finally available for public access via NIH Clinical Center Box. Find more details in the README file.
- [Jul. 26th, 2017] I am invited to give an talk in Medical Computer Vision and Health Informatics Workshop CVPR 2017 about our recent work on "Big Data, Weak Label and True Clinical Impacts for Radiology Imaging Diagnosis" (slides).
- [Jul.
24th, 2017] Our work
“DeepLesion:
Automated Deep Mining, Categorization and Detection of Significant
Radiology Image Findings using Large-Scale Clinical Lesion Annotations” is accepted as a
scientific poster in RSNA 2017
- [Mar. 07th, 2017] Our work on “ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases” is accepted as a spotlight in CVPR 2017.
- [Dec. 20th, 2016] Our work on “Unsupervised Joint Mining of Deep Features and Image Labels for Large-scale Radiology Image Annotation and Scene Recognition” is accepted in WACV 2017.
- [Nov. 26th, 2016] Our work “Automated Annotation of a Large Scale Radiology Image Database using Deep Learning” wins the trainee research prize in RSNA 2016.
- [July 26th, 2016] Our work “Automated Annotation of a Large Scale Radiology Image Database using Deep Learning” is accepted as a scientific poster in RSNA 2016.
- [July 25th, 2016] Our medical image categorization work wins the NIH Fellows Award of Research Excellence (FARE) 2017 competition.
- [Apr. 29th, 2016] Our paper “Automatic Lymph Node Cluster Segmentation using Holistically-Nested Networks and Structured Optimization in CT images” is early accepted in MICCAI 2016.
- [July 20th, 2015] I start my postdoctoral fellowship in NIH.
