Deep Cervix Model Development from Heterogeneous and Partially Labeled Image Datasets

01/18/2022
by   Anabik Pal, et al.
0

Cervical cancer is the fourth most common cancer in women worldwide. The availability of a robust automated cervical image classification system can augment the clinical care provider's limitation in traditional visual inspection with acetic acid (VIA). However, there are a wide variety of cervical inspection objectives which impact the labeling criteria for criteria-specific prediction model development. Moreover, due to the lack of confirmatory test results and inter-rater labeling variation, many images are left unlabeled. Motivated by these challenges, we propose a self-supervised learning (SSL) based approach to produce a pre-trained cervix model from unlabeled cervical images. The developed model is further fine-tuned to produce criteria-specific classification models with the available labeled images. We demonstrate the effectiveness of the proposed approach using two cervical image datasets. Both datasets are partially labeled and labeling criteria are different. The experimental results show that the SSL-based initialization improves classification performance (Accuracy: 2.5 from both datasets during SSL further improves the performance (Accuracy: 1.5 min). Further, considering data-sharing restrictions, we experimented with the effectiveness of Federated SSL and find that it can improve performance over the SSL model developed with just its images. This justifies the importance of SSL-based cervix model development. We believe that the present research shows a novel direction in developing criteria-specific custom deep models for cervical image classification by combining images from different sources unlabeled and/or labeled with varying criteria, and addressing image access restrictions.

READ FULL TEXT
research
09/06/2023

Improving Image Classification of Knee Radiographs: An Automated Image Labeling Approach

Large numbers of radiographic images are available in knee radiology pra...
research
01/11/2021

Resolution-Based Distillation for Efficient Histology Image Classification

Developing deep learning models to analyze histology images has been com...
research
03/17/2023

Data-Centric Learning from Unlabeled Graphs with Diffusion Model

Graph property prediction tasks are important and numerous. While each t...
research
03/25/2022

Intelligent Masking: Deep Q-Learning for Context Encoding in Medical Image Analysis

The need for a large amount of labeled data in the supervised setting ha...
research
10/02/2020

Smart-Inspect: Micro Scale Localization and Classification of Smartphone Glass Defects for Industrial Automation

The presence of any type of defect on the glass screen of smart devices ...
research
06/10/2015

LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop

While there has been remarkable progress in the performance of visual re...
research
03/27/2018

Towards Human-Machine Cooperation: Self-supervised Sample Mining for Object Detection

Though quite challenging, leveraging large-scale unlabeled or partially ...

Please sign up or login with your details

Forgot password? Click here to reset