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Project Descriptions
Spring 2025

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Large scale machine learning projects for medical imaging and natural language processing in Pathology

Iain Carmichael, Professor  
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Closed. This professor is continuing with Fall 2024 apprentices on this project; no new apprentices needed for Spring 2025.

Work with UCSF on large scale machine learning projects for medical imaging and text processing in Pathology. The ultimate aim of this collaboration is to develop clinically impactful deep learning algorithms for disease diagnosis/prognosis using massive (e.g. 100,000x100,000 pixel) whole slide images (https://www.pixelscientia.com/article-finding-prognostic-patterns-in-gigapixel-images.html) that have only recently become available at scale through UCSF's impressive digital pathology operation. These projects will involve close collaboration with an interdisciplinary team of statisticians, computer scientists, and clinicians.

Role: Possible tasks may include working with our team to

Build deep learning pipelines for massive scale supervised learning model for clinical imaging data

Train “foundational models” for histopathology data with self-supervised learning approaches

Construct new deep learning architectures that handle the size and complexity of histopathology images

Develop deep learning approaches for semantic segmentation of cancerous tissue

Develop interpretability algorithms to understand how our deep learning models are working and uncover new insights into cancer biology

Contribute to software packages

Write manuscripts

Qualifications: Previous deep learning experience is necessary for most (though not necessarily all) projects.

Experience in computer vision is appreciated.

Proficiency in Python (including standard scientific python libraries like numpy, matplotlib, etc) is required. Previous software engineering experience in industry is desired (e.g. internships).

Working knowledge of machine learning at the advanced undergraduate course level is required.

Hours: to be negotiated

Off-Campus Research Site: Remote

Related website: https://idc9.github.io/group.html

 Digital Humanities and Data Science   Mathematical and Physical Sciences

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