PhD Candidate @ Heidelberg University
Medical oncology · Bioinformatics · Medical image analysis · Computational pathology
I am a PhD Candidate at Heidelberg University, working in experimental and translational head and neck oncology. My research background combines oncology, molecular biology, bioinformatics, and medical image analysis.
My current interests focus on applying deep learning and computational methods to biomedical data, including histopathology image classification, medical image segmentation, single-cell RNA sequencing, bulk RNA-seq analysis, and multi-omics integration.
- Medical image analysis
- Computational pathology
- Histopathology image classification
- 3D medical image segmentation
- Head and neck squamous cell carcinoma
- Cancer genomics and transcriptomics
- Single-cell RNA-seq and bulk RNA-seq analysis
- Multi-omics integration
- Translational oncology
Heidelberg University
PhD, Experimental and Translational Head and Neck Oncology
Apr. 2019 - Present
Relevant coursework:
- Genome sequencing analysis
- Data analysis
- Data visualization
- Medical image analysis
PhD Researcher, Head and Neck Oncology
Apr. 2019 - Present
- Investigating subgroup-specific differences in the mutational landscape, epigenome, and transcriptome of head and neck squamous cell carcinoma.
- Working with scRNA-seq and bulk RNA-seq data using R and Python.
- Applying multi-omics integration for tumor subgroup characterization.
- Conducted machine learning-based quantitative analysis of HDV-infected cells in IHC-stained samples.
- Participated in antibody development workflows, including antibody production, preparation, and ELISA testing.
A deep learning project for colorectal cancer tissue classification using H&E-stained histopathology image patches.
- Model: EfficientNet-B0 with ImageNet-pretrained weights
- Dataset: NCT-CRC-HE-100K and CRC-VAL-HE-7K
- Task: 9-class colorectal tissue classification
- Result: 96.76% test accuracy
Freezing Depth and Stain Augmentation for Robust Transfer Learning under Histopathology Domain Shift
A controlled factorial study examining how network freezing depth and stain augmentation interact to affect cross-domain generalization of EfficientNet-B0 on colorectal histopathology tissue classification.
Bioinformatic analysis of head and neck squamous cell carcinoma patient subgroups, focusing on differences in mutation patterns, epigenomic profiles, and transcriptomic features.
Machine learning-based quantitative analysis of IHC-stained biomedical samples, with a focus on reproducible image processing and cell-level quantification.
- Plath M., Gass J., Hlevnjak M., Li Q. et al. Unraveling most abundant mutational signatures in head and neck cancer. International Journal of Cancer. 2021;148(1):115-127.
- Li C., Li Q., Cai Y., et al. Overexpression of angiopoietin 2 promotes oral squamous cell carcinoma formation via EMT-induced angiogenesis. Cancer Gene Therapy. 2016;23(9):295-302.
🇨🇳 Chinese (Native) · 🇬🇧 English · 🇩🇪 German
ResearchGate: Qiaoli Li · GitHub: Qiaoli-Li-Res