Mingren Shen

I am a Ph.D. student in Materials Science at UW-Madison, where I work on applying computer vision and machine learning methods into material and related area problems.

Before UW-Madison, I got B.S. Physics at Nanjing University and M.S. Physics at University of Chinese Academy of Sciences where I was advised by Dr. Ke Chen and Dr. Mingcheng Yang .

I obtained M.S. in Computer Sciences at UW-Madison in May 2019. Cheers!

I obtained M.S. in Material Sciences and Engineering at UW-Madison in December 2020. Cheers!

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Current Research

I'm interested in computer vision, machine learning, image processing, virtual reality, and computational material and biology. Much of my research is about applying newly developed computational methods and models for the physical world including DNA, TEM images etc.

fasterRCNN

Automated Defect Recognition in Electron Microscopy Images
Minren Shen, 2018
Advisor: Prof. Dane Morgan, Department of Materials Science and Engineering, UW-Madison


I build an automated image analysis system for identifying dislocation loops in irradiated steels. Faster R-CNN module in ChainerCV is used to identify material defects in Electron Microscopy Images. The new system can detect TEM images faster and more stable compared to human level experts.

Medical

Identifying Active Extravasation on Arteriograms with Deep Learning
Minren Shen, 2018
Advisor: Prof. Dane Morgan, Prof. Po-Ling Loh, Prof. Varun Jog, MD. Mark Kleedehn, UW-Madison


A two-stage method was used to solve the extravasation detection problem, where the first stage was used to classify whether a bleed was present and the second stage where an object detector was trained to identify the site of bleeding. ResNet-152 was used as the first stage classifier and Faster R-CNN was used as the second stage object detector. The first stage of the algorithm was 86% accuracy. The second stage of the algorithm correctly identified 5 of the 10 sites of bleeding.
The results are submitted to a radiologist conference(CIRSE2019).

Publications Lists and Abstract
  1. Mingren Shen, Guanzhao Li, Dongxia Wu, Yudai Yaguchi, Jack C. Haley, Kevin G. Field, and Dane Morgan. "A deep learning based automatic defect analysis framework for In-situ TEM ion irradiations." Computational Materials Science 197 (2021): 110560.
  2. Gurbani, Sidharth, Dane Morgan, Varun Jog, Leo Dreyfuss, Mingren Shen, Arighno Das, E. Jason Abel, and Meghan G. Lubner. "Evaluation of radiomics and machine learning in identification of aggressive tumor features in renal cell carcinoma (RCC)." Abdominal Radiology (2021): 1-11.
  3. Lawrence, Nick, Mingren Shen, Ruiqi Yin, Cloris Feng, and Dane Morgan. Exploring Generative Adversarial Networks for Image-to-Image Translation in STEM Simulation. arXiv preprint arXiv:2010.15315 (2020).
  4. Xiaoyu Sun, Nathaniel J. Krakauer, Alexander Politowicz, Wei‐Ting Chen, Qiying Li, Zuoyi Li, Xianjia Shao, Alfred Sunaryo, Mingren Shen, James Wang, Dane Morgan. Assessing Graph‐based Deep Learning Models for Predicting Flash Point. Molecular Informatics (2020), 39 , 1900101.
  5. Liu, Yilin, Gregory R. Kirk, Brendon M. Nacewicz, Martin A. Styner, Mingren Shen, Dong Nie, Nagesh Adluru, Benjamin Yeske, Peter A. Ferrazzano, and Andrew L. Alexander. Harmonization and Targeted Feature Dropout for Generalized Segmentation: Application to Multi-site Traumatic Brain Injury Images. In Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data, pp. 81-89. Springer, Cham, 2019.
  6. Luo, Guan-Zheng, Ziyang Hao, Liangzhi Luo, Mingren Shen, Daniela Sparvoli, Yuqing Zheng, Zijie Zhang et al. N 6-methyldeoxyadenosine directs nucleosome positioning in Tetrahymena DNA. Genome biology 19, no. 1 (2018): 200.
  7. Mingren Shen, Fangfu Ye, Rui Liu, Ke Chen, Mingcheng Yang, and Marisol Ripoll. Chemically driven fluid transport in long microchannels. The Journal of chemical physics 145, no. 12 (2016): 124119
  8. Mingren Shen, Liu Rui, Hou Mei-Ying, Yang Ming-Cheng, and Chen Ke. Mesoscale simulation of self-diffusiophoretic microrotor. ACTA PHYSICA SINICA 65, no.17 (2016).
ML-RCC

A deep learning based automatic defect analysis framework for In-situ TEM ion irradiations
Mingren Shen, Guanzhao Li, Dongxia Wu, Yudai Yaguchi, Jack C.Haley, Kevin G.Field, Dane Morgan, 2021

Videos captured using Transmission Electron Microscopy (TEM) can encode details regarding the morphological and temporal evolution of a material by taking snapshots of the microstructure sequentially. However, manual analysis of such video is tedious, error-prone, unreliable, and prohibitively time-consuming if one wishes to analyze a significant fraction of frames for even videos of modest length. In this work, we developed an automated TEM video analysis system for microstructural features based on the advanced object detection model called YOLO and tested the system on an in-situ ion irradiation TEM video of dislocation loops formed in a FeCrAl alloy. The system provides analysis of features observed in TEM including both static and dynamic properties using the YOLO-based defect detection module coupled to a geometry analysis module and a dynamic tracking module. Results show that the system can achieve human comparable performance with an F1 score of 0.89 for fast, consistent, and scalable frame-level defect analysis. This result is obtained on a real but exceptionally clean and stable data set and more challenging data sets may not achieve this performance. The dynamic tracking also enabled evaluation of individual defect evolution like per defect growth rate at a fidelity never before achieved using common human analysis methods. Our work shows that automatically detecting and tracking interesting microstructures and properties contained in TEM videos is viable and opens new doors for evaluating materials dynamics.

Paper Link

ML-RCC

Evaluation of radiomics and machine learning in identification of aggressive tumor features in renal cell carcinoma (RCC)
Gurbani, Sidharth, Dane Morgan, Varun Jog, Leo Dreyfuss, Mingren Shen, Arighno Das, E. Jason Abel, and Meghan G. Lubner, 2021

The purpose of this study was to evaluate the use of CT radiomics features and machine learning analysis to identify aggressive tumor features, including high nuclear grade (NG) and sarcomatoid (sarc) features, in large renal cell carcinomas (RCCs). The XG Boost Model performed best on the tested data. After manual and machine feature extraction, models consisted of 3, 7, 5, 10 radiomics features for NC sarc, PV sarc, NC NG and PV NG, respectively. The area under the receiver operating characteristic curve (AUC) for these models was 0.59, 0.65, 0.69 and 0.58 respectively. The multichannel NG model extracted 6 radiomic features using the feature selection strategy and showed an AUC of 0.67. Statistically significant but weak associations between aggressive tumor features (high nuclear grade, sarcomatoid features) in large RCC were identified using 3D radiomics and machine learning analysis.

Paper Link

STEM-GAN

Exploring Generative Adversarial Networks for Image-to-Image Translation in STEM Simulation
Nick Lawrence, Mingren Shen, Ruiqi Yin, Cloris Feng, Dane Morgan, 2020


The use of accurate scanning transmission electron microscopy (STEM) image simulation methods require large computation times that can make their use infeasible for the simulation of many images. Other simulation methods based on linear imaging models, such as the convolution method, are much faster but are too inaccurate to be used in application. In this paper, we explore deep learning models that attempt to translate a STEM image produced by the convolution method to a prediction of the high accuracy multislice image. We then compare our results to those of regression methods. We find that using the deep learning model Generative Adversarial Network (GAN) provides us with the best results and performs at a similar accuracy level to previous regression models on the same dataset. Codes and data for this project can be found in this GitHub repository, this https URL

Paper Link

Turbine

Assessing Graph‐based Deep Learning Models for Predicting Flash Point
Xiaoyu Sun, Nathaniel J. Krakauer, Alexander Politowicz, Wei‐Ting Chen, Qiying Li, Zuoyi Li, Xianjia Shao, Alfred Sunaryo, Mingren Shen, James Wang, Dane Morgan, 2020


Flash points of organic molecules play an important role in preventing flammability hazards and large databases of measured values exist, although millions of compounds remain unmeasured. To rapidly extend existing data to new compounds many researchers have used quantitative structure‐property relationship (QSPR) analysis to effectively predict flash points. In recent years graph‐based deep learning (GBDL) has emerged as a powerful alternative method to traditional QSPR. In this paper, GBDL models were implemented in predicting flash point for the first time. We assessed the performance of two GBDL models, message‐passing neural network (MPNN) and graph convolutional neural network (GCNN), by comparing against 12 previous QSPR studies using more traditional methods. Our result shows that MPNN both outperforms GCNN and yields slightly worse but comparable performance with previous QSPR studies. The average R^2 and Mean Absolute Error (MAE) scores of MPNN are, respectively, 2.3 % lower and 2.0 K higher than previous comparable studies. To further explore GBDL models, we collected the largest flash point dataset to date, which contains 10575 unique molecules. The optimized MPNN gives a test data R^2 of 0.803 and MAE of 17.8 K on the complete dataset. We also extracted 5 datasets from our integrated dataset based on molecular types (acids, organometallics, organogermaniums, organosilicons, and organotins) and explore the quality of the model in these classes.

Paper Link

Turbine

Harmonization and Targeted Feature Dropout for Generalized Segmentation: Application to Multi-site Traumatic Brain Injury Images
Yilin Liu, Gregory R. Kirk, Brendon M. Nacewicz, Martin A. Styner, Mingren Shen, Dong Nie, Nagesh Adluru, Benjamin Yeske, Peter A. Ferrazzano, Andrew L. Alexander, 2019


While learning based methods have brought extremely promising results in medical imaging, a major bottleneck is the lack of generalizability. Medical images are often collected from multiple sites and/or protocols for increasing statistical power, while CNN trained on one site typically cannot be well-transferred to others. Further, expert-defined manual labels for medical images are typically rare, making training a dedicated CNN for each site unpractical, so it is important to make best use of the limited labeled source data. To address this problem, we harmonize the target data using adversarial learning, and propose targeted feature dropout (TFD) to enhance the robustness of the model to variations in target images. Specifically, TFD is guided by attention to stochastically remove some of the most discriminative features. Essentially, this technique combines the benefits of attention mechanism and dropout, while it does not increase parameters and computational costs, making it well-suited for small neuroimaging datasets. We evaluated our method on a challenging Traumatic Brain Injury (TBI) dataset collected from 13 sites, using labeled source data of only 14 healthy subjects. Experimental results confirmed the feasibility of using the Cycle-consistent adversarial network for harmonizing multi-site MR images, and demonstrated that TFD further improved the generalization of the vanilla segmentation model on TBI data, reaching comparable accuracy with that of the supervised learning. The code is available at here

Paper Link

Turbine

Diffusive-Flux-Driven Microturbines by Fore-and-Aft Asymmetric Phoresis
Mingren Shen, Rui Liu, Ke Chen, and Mingcheng Yang, 2019


We designed the microscale turbine that is powered purely by an external diffusive heat or mass flux. The turbine rotates perpendicular to the external thermal or chemical gradient due to fore-and-aft asymmetric thermophoresis or diffusiophoresis. Thus, its performance does not need alignment. The prototype turbine is validated by means of computer simulations. The results show that the angular velocity of the turbine sensitively depends on its structure and interactions with the solvent. This perpendicular-axis phoretic turbine provides large flexibility and the possibility to exploit existing thermal or chemical energies at small scales.

Paper Link

DNA

N6-methyldeoxyadenosine directs nucleosome positioning in Tetrahymena DNA
Guan-Zheng Luo, Ziyang Hao, Liangzhi Luo, Minren Shen, Daniela Sparvoli, Yuqing Zheng, Zijie Zhang, Xiaocheng Weng, Kai Chen, Qiang Cui, Aaron P. Turkewitz and Chuan He, 2018


I use large scale GPU accelerated molecular dynamics (MD) simulations to study mechanical property changes due to N6-methyldeoxyadenosine of DNA chains. The results shows knockout of a potential 6mA methyltransferase leads to a transcriptome-wide change of gene expression.

Paper Link

JCP2016

Chemically driven fluid transport in long micro channels
Mingren Shen, Fangfu Ye, Rui Liu, Ke Chen, Mingcheng Yang, and Marisol Ripoll, 2016


I show that a concentration drop across micro channels with periodically inhomogeneous boundary walls can laterally transport fluids over arbitrarily long distances along the micro channel. This work thus presents new insight into the fluid transport in long micro channels commonly found in nature and is useful for designing novel micro- or nanofluidic pumps.

Paper Link

CHN

Mesoscale simulation of self-diffusiophoretic micro rotor
Mingren Shen, Rui Liu, Mei-Ying Hou , Ming-Cheng Yang , Ke Chen, 2016


I employ hybrid molecular dynamics (MD) simulations and multi-particle collision dynamics (MPC) to investigate the motion of micro rotors. The rotational direction and speed of the micro rotor are determined by bead-solvent interactions, the rotor geometry, the solvent viscosity and the catalytic reaction ratio.

Paper Link(In Chinese)

Conference
MiNES2021: Discerning the effects of solute additions in FeCrAl on dislocation dynamics under irradiation using a Machine Learning object detection algorithm

American Nuclear Society (ANS), September 19–23, 2021 in Pittsburgh, Pennsylvania, USA

Priyam Patki, Mingren Shen, Yudai Yaguchi, Jack Haley, Dane Morgan, Kevin Field

Poster

SAR21: Use of radiomics and machine learning for identification of mucinous pancreas cysts

Society of Abdominal Radiology, 2021, Virtual Meeting

Adam Awe, Michael Vanden Heuvel, Tianyuan Yuan, Mingren Shen, Victoria Rendell (MD), Agrima Kampani, Shanchao Liang, Dane Morgan (PhD), Emily Winslow (MD), and Meghan Lubner (MD)

Oral Presentation

ARRS21: Diagnostic potential of machine learning analysis and radiomics for pancreatic cysts

American Roentgen Ray Society, 2021,

Adam Awe, Michael Vanden Heuvel, Tianyuan Yuan, Mingren Shen, Victoria Rendell, Emily Winslow, Meghan Lubner

Oral Presentation

MS&T19: Automated Defect Detection in Electron Microscopy with Machine Learning

Materials Science & Technology 2019 - Data Science for Material Property Interpretation (09/29/2019 - 10/03/2019, Portland, OR, USA)

Dane Morgan, Mingren Shen, Wei Li, Kevin Field

Oral Report

CIRSE 2019: Identifying active extravasation on arteriograms using artificial intelligence

Cardiovascular and Interventional Radiological Society of Europe (CIRSE) (09/07/2019 - 09/11/2019, Barcelona, Spain)

Mingren Shen, Mesut Ozturk, Po-Ling Loh, Varun Jog, Paul Laeseke, Dane Morgan, Mark Kleedehn

Oral Report: 2306.7 - Identifying active extravasation on arteriograms using artificial intelligence

Course Projects
CS744

Query time optimized video inference system
Mingren Shen, Shuoxuan Dong, Xiuyuan He, 2018


Optimizing the latency of a two CNNs video inference system by reusing the intermediate results of first CNN(ResNet50 ) to accelerate the calculation of second CNN( ResNet152 ). We successfully achieved 18% latency decrease without sacrificing the accuracy of the model.
Final Report

CS760

Improvement of Twitter gender classifier using user mobility features
Xinyi Liu , Mingren Shen , Faust Shi, 2017


We collected Twitter data from St Louis, MO to build a user gender classifier including Naïve Bayes Network, Random Forest, Ada Boost and Multi-Layer Neural Network (Python: scikit-learn) and test user mobility feature effects for those classifier by adding those geo features.

Final Report

Personal Projects
BBQ

BBQ:Bounding Box Quality Checker
Mingren Shen, Xiuyuan He, 2018


BBQ standing bounding box qualifier which is a web service that examines the matching between ground truth bounding boxes and the predicted bounding boxes and produces a prediction quality report and debugging tools for object detection algorithm. A lot of work of object detection methods are focusing on how to generate bounding box annotations. However, few have talked about how to debug or check where the object detection model fails. This Hackathon project is a tool that can help people actually see where the object detection algorithm fails.
Github

UTD

Driver Test Schedule System
Mingren Shen , Yang Yang, Jiachen Shen, 2016


This project helps reminder the users when there are personalized available space for their driving test in College Town of TAMU. The project is build on Ruby on Rails,Ruby(4.2.2),Python.

Github