Paper Info
Title
Attention Based Deep Multiple Instance Learning Approach for Lung Cancer Prediction using Histopathological Images
Abstract
Deep Neural Networks using histopathological images as an input currently embody one of the gold standards in automated lung cancer diagnostic solutions, with Deep Convolutional Neural Networks achieving the state of the art values for tissue type classification. One of the main reasons for such results is the increasing availability of voluminous amounts of data, acquired through the efforts employed by extensive projects like The Cancer Genome Atlas. Nonetheless, whole slide images remain weakly annotated, as most common pathologist annotations refer to the entirety of the image and not to individual regions of interest in the patient's tissue sample. Recent works have demonstrated Multiple Instance Learning as a successful approach in classification tasks entangled with this lack of annotation, by representing images as a bag of instances where a single label is available for the whole bag. Thus, we propose a bag/embedding-level lung tissue type classifier using Multiple Instance Learning, where the automated inspection of lung biopsy whole slide images determines the presence of cancer in a given patient. Furthermore, we use a post-model interpretability algorithm to validate our model's predictions and highlight the regions of interest for such predictions.
Year
DOI
Venue
2021
10.1109/EMBC46164.2021.9631000
2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC)
Keywords
DocType
Volume
Deep Learning, Multiple Instance Learning, Lung Cancer, Histology Images
Conference
2021
ISSN
Citations 
PageRank 
1557-170X
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Joao Moranguinho100.34
Tânia Pereira2248.61
Bernardo Ramos300.34
Joana Morgado401.01
Jose Luis Costa500.68
Helder P Oliveira601.01