HemeAI Lab
Translational Medical Artificial Intelligence for Hematological Diseases
HemeAI Lab
Artificial Intelligence and Computational Pathology for Hematological Diseases
The HemeAI lab develops software and artificial intelligence tools for applications in digital
hematopathology at Memorial Sloan Kettering Cancer Center.
Our aim is to use AI and computational tools to improve outcomes for patients with leukemias and other
diseases of the blood and bone marrow. Our approach
is to build real-world datasets (and software for annotating them), train deep-learning models that solve
current clinical problems, and
write software to the clinical deployment of these algorithms.
We are a new lab at MSKCC and are actively recruiting MS/PhD students, post-docs, clinical fellows, data scientists and software developers.
AI for fast and accurate segmentation of cells from marrow aspirate digital slides .
AI assisted, clinical software to assist in bone marrow aspirate counting and classification.
A teaching platform for hematopathology aimed at pathologists, oncologists, clinical laboratory scientists, cytotechnologists, fellows, residents and students.
Set of tools for extracting useful clinical information from hematopathology clinical reports useful for case identification and as data labeling.
Region of interest classification/detection for bone marrow aspirates.
Dr. Goldgof is an assistant professor at Memorial Sloan Kettering Cancer Center and directs artificial intelligence and digital pathology for the Hematopathology Service. He did his residency at the University of California, San Francisco (UCSF), with post-doctoral work in the laboratory of Professor Atul Butte. He received his MD and PhD degrees at the University of California, San Diego. He received his BS and MS at Stanford University in Computer Science and Bioengineering, respectively. Dr. Goldgof is board-certified in Clinical Pathology.
Harry is a PhD student in Biomedical Informatics Program at UCSF being co-advised with Professor Atul Butte. He grew up in the beautiful coastal city of Dalian and completed his undergraduate degree at the Nankai University in China. Pursuing a PhD at UCSF, he is actively exploring the interface of biomedical researches and artificial intelligence for achieving precision medicine. In the lab, he is focusing on developing deep-learning based models for bone marrow image classification and segmentation.
Jacob is a software developer working on the HemeLabel software suite. He works on both client-side and server-side projects, UI/UX design and cloud deployment.
Neo Yin is a PhD student in Statistics at the University of California, Berkeley. He completed his undergraduate degree in Mathematics and Philosophy at the University of Toronto. Neo’s current research interest is focused on computational pathology, specifically the development of deep learning-based tools to aid in diagnostic tasks and self-supervised methods for learning representations of whole slide image patches.
Irem works as a research fellow on the Hematopathology Service at Memorial Sloan Kettering Cancer Center. After finishing medical school, she began her pathology residency in Turkey. Before joining the MSKCC, she worked as a Cytopathology fellow in Turkey. Her main area of research was the assessment of hematologic tumors including lymphomas and leukemias. Her current research focuses mostly on bone marrow aspirates and is in the fields of hematopathology and digital pathology.
Siddharth is a ML scientist at Memorial Sloan Kettering Cancer Center and is building and productionizing multiple instance deep learning models for whole slide images. He has a MS in Robotics from Columbia University and brings a background in ML research and software engineering from research labs in supply chain logistics, robotics and autonomous driving.
Dylan Webb is a Statistics PhD student at the University of California, Berkeley. He completed his undergraduate degree in Applied Mathematics at Brigham Young University. In the lab he is developing computer vision and deep-learning-based tools to help pathologists predict patient outcomes from peripheral blood smears.
Sean is a post-doctoral researcher at Memorial Sloan Kettering Cancer Center. He received his PhD in Computer Science from Dartmouth College, the focus of which was the development of state-of-the-art deep learning methods for classifying fMRI data. He is currently working to expand such methods into additional health informatics.
Riya is a ML Research Scientist at Memorial Sloan Kettering Cancer Center. She works mostly on researching and developing deep learning as well as computer vision architectures for computational pathology. She also contributes to enhancing the cell-visualizations as well as search-related tools. Riya graduated from Columbia University with a background in Electrical and Computer Science Engineering (ML Research track) where she worked on Multiple Instance Learning and Multimodal Learning extensively.
