Evaluation framework for oncology foundation models (FMs)
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Updated
Jul 14, 2025 - Python
Evaluation framework for oncology foundation models (FMs)
a cutting-edge cell segmentation model specifically designed for single-molecule resolved spatial omics datasets. It addresses the challenge of accurately segmenting individual cells in complex imaging datasets, leveraging a unique approach based on graph neural networks (GNNs).
Using a Deep Learning CNN to detect acute lymphoblastic leukemia (ALL) from blood microscopy
VMAT modulation complexity index calculator based on https://github.com/victorgabr/ApertureComplexity
Point of care system for AMPATH clinics
Clinical oncology tumor board decision support system made by the Decider project.
Crossmapped phenotype ontologies for the oncology domain
Personalized Network-based Anti-Cancer Therapy Appointment
scMalignantFinder is a Python package specially designed for analyzing cancer single-cell RNA-seq datasets to distinguish malignant cells from their normal counterparts.
Clonal reconstruction from HTS data
A python framework for creating image-guided cancer patient digital twins.
Code from my work as Radiation Physicist Assistant at Cookeville Regional Medical Center
🐝 | From Data to Prognosis: Embedding Multimodal Oncology Data for Precision Medicine
Notes and procedures that I wrote as Radiation Physicist Assistant at Cookeville Regional Medical Center
A benchmark of histopathology Foundation Models on multi-stain Immunohistochemistry immune datasets AIM-FM workshop @ NeurIPS 24
A 3D lesion segmentation method on whole-body PET images including automated quality control.
PD-1 Targeted Antibody Discovery Using AI Protein Diffusion
A project focused on using single-cell RNA sequencing data (scRNA-seq) and pseudo time to improve colon cancer diagnosis and outcomes.
🧠 | Multimodal Integration of Oncology Data System
Read different files of TSO500 data analysis output and integrate the data.
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