DeepAI AI Chat
Log In Sign Up

Multi-Scale Attention-based Multiple Instance Learning for Classification of Multi-Gigapixel Histology Images

09/07/2022
by   Made Satria Wibawa, et al.
28

Histology images with multi-gigapixel of resolution yield rich information for cancer diagnosis and prognosis. Most of the time, only slide-level label is available because pixel-wise annotation is labour intensive task. In this paper, we propose a deep learning pipeline for classification in histology images. Using multiple instance learning, we attempt to predict the latent membrane protein 1 (LMP1) status of nasopharyngeal carcinoma (NPC) based on haematoxylin and eosin-stain (H E) histology images. We utilised attention mechanism with residual connection for our aggregation layers. In our 3-fold cross-validation experiment, we achieved average accuracy, AUC and F1-score 0.936, 0.995 and 0.862, respectively. This method also allows us to examine the model interpretability by visualising attention scores. To the best of our knowledge, this is the first attempt to predict LMP1 status on NPC using deep learning.

READ FULL TEXT

page 7

page 11

page 12

08/15/2022

Cross-scale Attention Guided Multi-instance Learning for Crohn's Disease Diagnosis with Pathological Images

Multi-instance learning (MIL) is widely used in the computer-aided inter...
09/19/2023

Multi-Stain Self-Attention Graph Multiple Instance Learning Pipeline for Histopathology Whole Slide Images

Whole Slide Images (WSIs) present a challenging computer vision task due...
05/17/2018

Terabyte-scale Deep Multiple Instance Learning for Classification and Localization in Pathology

In the field of computational pathology, the use of decision support sys...
04/07/2020

Bayesian aggregation improves traditional single image crop classification approaches

Machine learning (ML) methods and neural networks (NN) are widely implem...

Code Repositories

MultiAttentionMIL

supplementary code for paper:


view repo