Deep Learning for Histopathology and Broad Utility Biomedical Tools
Satwik Rajaram Lab
Two projects are available in the Rajaram Lab and in collaboration with the UTSW BioHPC:
1) Adversarial Attacks on Deep Learning Models for Clinical Grade Histopathology
Deep learning has the potential to revolutionize histopathological diagnoses, yet in practice, few models reach the level of reliability needed for clinical adoption. Given the difficulty of generating diverse training data sets, most models tend to be overly sensitive to experimental modalities (e.g. microscope or staining parameters) and fail to generalize to variations they would experience in the field. Current approaches meant to overcome these limitations are largely based on simplistic models of image variation and fail to be sufficiently challenging. The Rajaram Lab (https://www.rajaramlab.org) aims to develop data-driven adversarial attacks designed precisely to identify blind-spots of existing models, thereby forcing these models to become robust against the variations that are inevitable in practical settings. In this way, we hope to generate the next generation of histopathology models with clinical grade reliability. We are looking for motivated undergraduates with previous experience in deep-learning: comfortable in Tensorflow or PyTorch and familiar with the theory of deep learning. No previous experience in biology or histopathology is required, but some exposure to adversarial approaches would be a definite advantage.
2) Enable Reproducible Deep Learning for BioMedical Science
The bedrock of biomedical science is reproducibility. Yet, the reality of cutting edge research is that the data, models and questions are highly dynamic. So, while deep learning approaches have shown great promise, the cascading set of choices involved (training data, model choice, hyper-parameters, choice of augmentation etc) has meant that currently these pipelines typically do not reach the level of accountability required by the scientific community. The Rajaram Lab (https://www.rajaramlab.org) in collaboration with the UTSW BioHPC (https://portal.biohpc.swmed.edu) aims to develop a framework optimized for generating reproducible deep-learning pipelines in biomedicine. By building on existing frameworks such DVC (dvc.org), we aim to simultaneously perform version control on data, code and models, thereby allowing us to know exactly what went into developing a specific model, and to compare different models. As the work will be developed within a high-performance computing environment to support ongoing research, we expect that it will address several issues specific to biomedical research, and shall be of broad scientific utility. We are looking for an undergraduate passionate about deep-learning and reproducible research to spearhead this project.
For more information about these projects email Satwik Rajaram.
