MNIST Handwritten Classification
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Machine Learning course project
Posts by Collection
portfolio
Echo Cancellation
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Machine Learning course project: Solve the adaptive echo calcellation problem, such that the noise is removed from music
Switchgrass Genotype Classification using Hyperspectral Imagery
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Master Thesis
Python-based CyTOF processing and analyzing package
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Machine Learning course project: Solve the adaptive echo calcellation problem, such that the noise is removed from music
Time-series forecasting
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Time-series forecasting - A winning contribution to the Siemens’ Tech For Sustainability 2023 campaign in the Swarm Behaviour on the Grid track
Advancing Plant Phenotyping with PlantCV: An Open-Source Image Analysis Software Package
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February 2020 ~ September 2021
This project was centered around PlantCV (Plant Computer Vision), an open-source software package meticulously designed for plant phenotyping analysis. Developed in Python and integrating advanced image processing libraries such as OpenCV (Open Source Computer Vision Library), PlantCV aims to provide plant scientists and researchers with a powerful, flexible, and user-friendly tool for automating and quantifying the extraction of plant phenotypic information from various image data.
Enhanced Detection and Classification of Cell Nuclei in H&E Stained Pathology Images Using Mask R-CNN
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October 2021 ~ December 2021
This project focused on the application of Mask R-CNN, a state-of-the-art model for instance segmentation tasks, to detect and classify common types of cell nuclei in H&E (Hematoxylin and Eosin) stained pathology images of Non-Small Cell Lung Cancer (NSCLC) and Breast Cancer. By leveraging transfer learning and customizing the loss function of Mask R-CNN, the project aimed to address the challenges posed by incomplete labeling in the dataset and improve the model’s performance in both detection and classification tasks.
Automated Prescription Parsing API development, maintain, and improvement (for the Japanese Market)
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September 2023 – October 2023
This project aimed to extend the capabilities of an existing in-house developed tool, OptiReader, designed for the automatic parsing of eyeglass prescriptions. Initially supporting the North American market, the project’s goal was to adapt the tool for the Japanese market, particularly for VR eyeglasses, by incorporating innovative document understanding technologies and custom solutions to handle unique prescription formats prevalent in Japan.
Time-series forecasting for a payment processing company
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May 2023
This project is actually a pre-interview project for data scientist. I have got a chance to perform data analysis and time-series modeling and forecasting.
publications
Root identification in minirhizotron imagery with multiple instance learning
Published in Machine Vision and Applications, 2020
This paper is about applying multiple instance learning for an image segmentation task (root segmentation) from minirhizotron images.
Recommended citation: "Yu, G., Zare, A., Sheng, H., Matamala, R., Reyes-Cabrera, J., Fritschi, F.B. and Juenger, T.E., 2020. Root identification in minirhizotron imagery with multiple instance learning. Machine Vision and Applications, 31, pp.1-13." /files/1903.03207.pdf
A Deep Learning Approach for Histology-Based Nuclei Segmentation and Tumor Microenvironment Characterization
Published in New Phytologist, 2023
This paper is about applying deep learning based approach for nuclei segmentation and tumor microenvironment characterization.
Recommended citation: Panda K, Mohanasundaram B, Gutierrez J, McLain L, Castillo SE, Sheng H, Casto A, Gratacós G, Chakrabarti A, Fahlgren N, Pandey S. The plant response to high CO2 levels is heritable and orchestrated by DNA methylation. New Phytologist. 2023 Jun;238(6):2427-39. https://nph.onlinelibrary.wiley.com/doi/full/10.1111/nph.18876
A Deep Learning Approach for Histology-Based Nuclei Segmentation and Tumor Microenvironment Characterization
Published in Pathology, 2023
This paper is about applying deep learning based approach for nuclei segmentation and tumor microenvironment characterization.
Recommended citation: Rong R, Sheng H, Jin KW, Wu F, Luo D, Wen Z, Tang C, Yang DM, Jia L, Amgad M, Cooper LA. A Deep Learning Approach for Histology-Based Nucleus Segmentation and Tumor Microenvironment Characterization. Modern Pathology. 2023 Aug 1;36(8):100196. https://www.sciencedirect.com/science/article/pii/S0893395223001011
Increasing the Throughput of Annotation Tasks Across Scales of Plant Phenotyping Experiments
Published in NAPPN2024, 2023
This paper is about applying deep learning based approach for nuclei segmentation and tumor microenvironment characterization.
Recommended citation: Sheng H, Gutierrez J, Schuhl H, Murphy KM, Acosta-Gamboa L, Gehan M, Fahlgren N. Increasing the Throughput of Annotation Tasks Across Scales of Plant Phenotyping Experiments. Authorea Preprints. 2023 Oct 19. https://www.techrxiv.org/doi/full/10.22541/essoar.169773045.57471797
talks
Talk 1 on Relevant Topic in Your Field
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This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.