Lung Cancer Diagnosis

Laura Daza
Angela Castillo
Maria Escobar
Mafe Roa
Pablo Arbeláez
Collaboration with Gustavo Pérez,
Dr. Bibiana Pinzón and Dr. Sergio Valencia from the Radiology Department, Fundación Santa Fe de Bogotá
and David Cuellar, Astrazeneca

Lung cancer is the second most common type of cancer in the world. The reason for this staggering mortality rate is the near absolute absence of apparent symptoms in patients of early lung cancer. Consequently, the vast majority of lung cancers worldwide are diagnosed in stages III and IV, when the efficacy of existing treatments and hence the chances of survival are seriously compromised. Although deep learning methods have pushed forward automated early lung cancer diagnosis in recent years, all existing datasets and challenges seek to diagnose the disease using exclusively visual data. However, specialists also take into consideration all their knowledge of the context and the patient's medical history. In this project we aim at creating a method that includes both visual and clinical information for early stage lung cancer diagnosis.

Presentation Video


LUCAS: LUng CAncer Screening with Multimodal Biomarkers

L. Daza, A. Castillo, M. Escobar, S. Valencia, B. Pinzón, and P. Arbeláez

WORKSHOP ON Multimodal Learning for Clinical Decision Support (ML-CDS) 2020

Automated lung cancer diagnosis using three-dimensional convolutional neural networks

G.Pérez and P. Arbeláez

Medical & Biological Engineering & Computing

Automated detection of lung nodules with three-dimensional convolutional neural networks

G. Pérez and P. Arbeláez

13th International Conference on Medical Information Processing and Analysis (SIPAIM), 2017


Google Latin America Research Award 2019

L. Daza and P. Arbeláez

We won the Google Latin America Research Awards (LARA) in 2019 with the project "Lung Nodule Detection and Malignancy Prediction Using Multimodal Neural Networks".

ISBI2018 Challenge

G. Pérez and P. Arbeláez

We won the first place at the international ISBI2018 Challenge of Lung Nodule Malignancy Prediction, Based on Sequential CT Scans.
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