Interpretable Fully Convolutional Classification of Intrapapillary Capillary Loops for Real-Time Detection of Early Squamous Neoplasia

In this work, we have concentrated our efforts on the interpretability of classification results coming from a fully convolutional neural network. Motivated by the classification of oesophageal tissue for real-time detection of early squamous neoplasia, the most frequent kind of oesophageal cancer in Asia, we present a new dataset and a novel deep learning method that by means of deep supervision and a newly introduced concept, the embedded Class Activation Map (eCAM), focuses on the interpretability of results as a design constraint of a convolutional network. We present a new approach to visualise attention that aims to give some insights on those areas of the oesophageal tissue that lead a network to conclude that the images belong to a particular class and compare them with those visual features employed by clinicians to produce a clinical diagnosis. In comparison to a baseline method which does not feature deep supervision but provides attention by grafting Class Activation Maps, we improve the F1-score from 87.3 attention maps.

READ FULL TEXT

page 2

page 4

page 7

research
03/14/2017

Fully Convolutional Networks to Detect Clinical Dermoscopic Features

We use a pretrained fully convolutional neural network to detect clinica...
research
11/24/2016

Weakly Supervised Cascaded Convolutional Networks

Object detection is a challenging task in visual understanding domain, a...
research
09/07/2023

Beyond attention: deriving biologically interpretable insights from weakly-supervised multiple-instance learning models

Recent advances in attention-based multiple instance learning (MIL) have...
research
08/17/2020

Exploiting Fully Convolutional Network and Visualization Techniques on Spontaneous Speech for Dementia Detection

In this paper, we exploit a Fully Convolutional Network (FCN) to analyze...
research
10/21/2020

Automating Abnormality Detection in Musculoskeletal Radiographs through Deep Learning

This paper introduces MuRAD (Musculoskeletal Radiograph Abnormality Dete...
research
11/25/2021

Extending the Relative Seriality Formalism for Interpretable Deep Learning of Normal Tissue Complication Probability Models

We formally demonstrate that the relative seriality model of Kallman, et...

Please sign up or login with your details

Forgot password? Click here to reset