In Search of Effective and Reproducible Clinical Imaging Biomarkers for Population Health and Oncology Applications of Screening, Diagnosis and Prognosis

Speaker: Dr. Le Lu
         Head of Medical AI R&D for Alibaba Group
         Researcher at DAMO Academy USA

Title:   "In Search of Effective and Reproducible Clinical Imaging
          Biomarkers for Population Health and Oncology Applications of
          Screening, Diagnosis and Prognosis"

Date:   Monday, 25 April 2022

Time:   10:00am - 11:00am (HKT)

Zoom link:
https://hkust.zoom.us/j/928308079?pwd=MW9wTCtlSDd2MnViZGdNd2oreUpXZz09

Meeting ID:     928 308 079
Passcode:       20212022


Abstract:

This talk will first give an overall on the work of employing deep
learning to permit novel clinical workflows in two population health
tasks, namely using conventional ultrasound for liver steatosis screening
and quantitative reporting; osteoporosis screening via conventional X-ray
imaging and "AI readers". These two tasks were generally considered as
infeasible tasks for human readers, but as proved by our scientific and
clinical studies and peer-reviewed publications, they are suitable for AI
readers. AI can be a supplementary and useful tool to assist physicians
for cheaper and more convenient/precision patient management. Next, the
main part of this talk describes a roadmap on three key problems in
pancreatic cancer imaging solution: early screening, precision
differential diagnosis, and deep prognosis on patient survival prediction.
(1) Based on a new self-learning framework, we train the pancreatic ductal
adenocarcinoma (PDAC) segmentation model using a larger quantity of
patients, with a mix of annotated/unannotated venous or multi-phase CT
images. Pseudo annotations are generated by combining two teacher models
with different PDAC segmentation specialties on unannotated images, and
can be further refined by a teaching assistant model that identifies
associated vessels around the pancreas. Our approach makes it technically
feasible for robust large-scale PDAC screening from multi-institutional
multi-phase partially-annotated CT scans. (2) We propose a holistic
segmentation-mesh classification network (SMCN) to provide patient-level
diagnosis, by fully utilizing the geometry and location information. SMCN
learns the pancreas and mass segmentation task and builds an anatomical
correspondence-aware organ mesh model by progressively deforming a
pancreas prototype on the raw segmentation mask. Our results are
comparable to a multimodality clinical test that combines clinical,
imaging, and molecular testing for clinical management of patients with
cysts. (3) Accurate preoperative prognosis of resectable PDACs for
personalized treatment is highly desired in clinical practice. We present
a novel deep neural network for the survival prediction of resectable PDAC
patients, 3D Contrast-Enhanced Convolutional Long Short-Term Memory
network (CE-ConvLSTM), to derive the tumor attenuation signatures from
CE-CT imaging studies. Our framework can significantly improve the
prediction performances upon existing state-of-the-art survival analysis
methods. This deep tumor signature has evidently added values (as a
predictive biomarker) to be combined with the existing clinical staging
system.


******************
Biography:

Le Lu received a PhD in 2007 from Johns Hopkins University. During his
first six years at Siemens, he made significant contributions to the
company's CT colonography and Lung CAD product lines. From 2013 to 2017,
Dr. Lu served as a staff scientist in the Radiology and Imaging Sciences
department of the National Institutes of Health Clinical Center. He then
went on to found Nvidia's medical image analysis group and he held the
position of senior research manager until June 2018. Since then, he had
been the Executive Director at PAII Inc., Bethesda Research lab, Maryland,
USA until July 2021, which has become one of the leading industrial
research labs in medical imaging. He is currently the head of Medical AI
R&D for Alibaba group, and a researcher at DAMO academy USA. He was the
main technical leader for two of the most-impactful public radiology image
dataset releases (NIH ChestXray14, NIH DeepLesion 2018). He won NIH
Clinical Center Director Award in 2017, NIH Mentor of the year award in
2015, and won numerous best paper awards in MICCAI and RSNA from 2016 to
2020 (13000+ citations). In 2021, he was elected into IEEE Fellow class
cited for his contribution to machine learning for cancer detection and
diagnosis, and MICCAI society board member (MICCAI-Industry Workgroup
Chair). He is currently an Associate Editor for IEEE Trans. Pattern
Analysis and Machine Intelligence, IEEE Signal Processing Letters and
Frontier in Oncology. In 2022, he was elected as an IEEE signal processing
society distinguished industry speaker.