Work

Scatter signatures in SFDI data enable breast surgical margin delineation via ensemble learning

Year: 2020

Type: conference-paper

Abstract: Margin assessment in gross pathology is becoming feasible as various explanatory deep learning-powered methods are able to obtain models for macroscopic textural information, tissue microstructure, and local surface optical properties. Unfortunately, each different method seems to lack enough diagnostic power to perform an adequate classification on its own. This work proposes using several separately trained deep convolutional networks, and averaging their responses, in order to achieve a better margin assessment. Qualitative leave-one-out cross-validation results are discussed for a cohort of 70 samples.

DOI: 10.1117/12.2546945

Authors: Arturo Pardo, Samuel S. Streeter, Benjamin W. Maloney, José M. López-Higuera, Brian W. Pogue, Olga M. Conde

Institutions: Universidad de Cantabria, Instituto de Investigación Marqués de Valdecilla, Dartmouth College, Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine, Centro de Investigación Biomédica en Red

Language: English

Topic: Optical Imaging and Spectroscopy Techniques

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

OpenAlex ID: W3007789045

FWCI: 0.821

Cites: 8

Cited by: 3

Related to: 10