Work

Kernel Self-Attention for Weakly-supervised Image Classification using Deep Multiple Instance Learning

Year: 2021

Type: conference-paper

Abstract: Not all supervised learning problems are described by a pair of a fixed-size input tensor and a label. In some cases, especially in medical image analysis, a label corresponds to a bag of instances (e.g. image patches), and to classify such bag, aggregation of information from all of the instances is needed. There have been several attempts to create a model working with a bag of instances, however, they are assuming that there are no dependencies within the bag and the label is connected to at least one instance. In this work, we introduce Self-Attention Attention-based MIL Pooling (SA-AbMILP) aggregation operation to account for the dependencies between instances. We conduct several experiments on MNIST, histological, microbiological, and retinal databases to show that SA-AbMILP performs better than other models. Additionally, we investigate kernel variations of Self-Attention and their influence on the results.

Source: IEEE Winter Conference on Applications of Computer Vision

DOI: 10.1109/wacv48630.2021.00176

Authors: Dawid Rymarczyk, Adriana Borowa, Jacek Tabor, Bartosz Zielinski

Institution: Jagiellonian University

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: European Commission, Fundacja na rzecz Nauki Polskiej, European Regional Development Fund

Awards: G2015427213, G5214980857, G735078438