Attention Based Deep Multiple Instance Learning Approach for Lung Cancer Prediction using Histopathological Images

• Type: Article in International Conference Proceedings Book
• Date: 2021

Authors

Moranguinho, J (Author) Other; Pereira, T (Author) Other; Ramos, B (Author) Other; Morgado, J (Author) Other; Costa, JL (Author) Other; Oliveira, HP (Author) FCUP

Conference proceedings International

Title: 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, EMBC 2021, Mexico, November 1-5, 2021

Pages: 2852-2855

Indexing

ISI Web of Knowledge - 10 Citations

Scopus - 14 Citations

Other information

Authenticus ID: P-00V-ZMQ

DOI: 10.1109/embc46164.2021.9631000

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 4