Alexander S. Baras

Abstract For decades, flow cytometry has allowed for single-cell profiling based on selected biomarkers and is widely used in both clinical and research settings. One major limitation of most conventional flow cytometry analyses is the dependency on a mostly manual gating process. This generally involves sequentially selecting biomarkers to isolate phenotype-associated cell populations, an approach that is both labor-intensive and prone to bias.

Abstract Potential clinical biomarkers are often assessed with Cox regressions or their ability to differentiate two groups of patients based on a single cutoff. However, both of these approaches assume a monotonic relationship between the potential biomarker and survival. Tumor mutational burden (TMB) is currently being studied as a predictive biomarker for immunotherapy, and a single cutoff is often used to divide patients.

Abstract Pancreatic fine-needle aspirations are the gold-standard diagnostic procedure for the evaluation of pancreatic ductal adenocarcinoma. A suspicion for malignancy can escalate towards chemotherapy followed by a major surgery and therefore is a high-stakes task for the pathologist. In this paper, we propose a deep learning framework, MIPCL, that can serve as a helpful screening tool, predicting the presence or absence of cancer.