Self-supervised learning for skin cancer diagnosis with limited training
data
- URL: http://arxiv.org/abs/2401.00692v1
- Date: Mon, 1 Jan 2024 08:11:38 GMT
- Title: Self-supervised learning for skin cancer diagnosis with limited training
data
- Authors: Hamish Haggerty and Rohitash Chandra
- Abstract summary: We show that a model pre-trained using a self-supervised learning algorithm known as Barlow Twins can outperform the conventional supervised transfer learning pipeline.
We achieve a mean test accuracy of 70% for self-supervised transfer in comparison to 66% for supervised transfer.
Our framework is applicable to cancer image classification models in the low-labelled data regime.
- Score: 0.2209921757303168
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Cancer diagnosis is a well-studied problem in machine learning since early
detection of cancer is often the determining factor in prognosis. Supervised
deep learning achieves excellent results in cancer image classification,
usually through transfer learning. However, these models require large amounts
of labelled data and for several types of cancer, large labelled datasets do
not exist. In this paper, we demonstrate that a model pre-trained using a
self-supervised learning algorithm known as Barlow Twins can outperform the
conventional supervised transfer learning pipeline. We juxtapose two base
models: i) pretrained in a supervised fashion on ImageNet; ii) pretrained in a
self-supervised fashion on ImageNet. Both are subsequently fine tuned on a
small labelled skin lesion dataset and evaluated on a large test set. We
achieve a mean test accuracy of 70\% for self-supervised transfer in comparison
to 66\% for supervised transfer. Interestingly, boosting performance further is
possible by self-supervised pretraining a second time (on unlabelled skin
lesion images) before subsequent fine tuning. This hints at an alternative path
to collecting more labelled data in settings where this is challenging - namely
just collecting more unlabelled images. Our framework is applicable to cancer
image classification models in the low-labelled data regime.
Related papers
- A Comparison of Self-Supervised Pretraining Approaches for Predicting
Disease Risk from Chest Radiograph Images [3.5880535198436156]
We present a comparison of semi- and self-supervised learning to predict mortality risk using chest x-ray images.
We find that a semi-supervised autoencoder outperforms contrastive and transfer learning in internal and external validation.
arXiv Detail & Related papers (2023-06-15T08:48:14Z) - Performance of GAN-based augmentation for deep learning COVID-19 image
classification [57.1795052451257]
The biggest challenge in the application of deep learning to the medical domain is the availability of training data.
Data augmentation is a typical methodology used in machine learning when confronted with a limited data set.
In this work, a StyleGAN2-ADA model of Generative Adversarial Networks is trained on the limited COVID-19 chest X-ray image set.
arXiv Detail & Related papers (2023-04-18T15:39:58Z) - Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New
Benchmark Study [75.05049024176584]
We present a benchmark study of the long-tailed learning problem in the specific domain of thorax diseases on chest X-rays.
We focus on learning from naturally distributed chest X-ray data, optimizing classification accuracy over not only the common "head" classes, but also the rare yet critical "tail" classes.
The benchmark consists of two chest X-ray datasets for 19- and 20-way thorax disease classification, containing classes with as many as 53,000 and as few as 7 labeled training images.
arXiv Detail & Related papers (2022-08-29T04:34:15Z) - Self-Supervised Learning as a Means To Reduce the Need for Labeled Data
in Medical Image Analysis [64.4093648042484]
We use a dataset of chest X-ray images with bounding box labels for 13 different classes of anomalies.
We show that it is possible to achieve similar performance to a fully supervised model in terms of mean average precision and accuracy with only 60% of the labeled data.
arXiv Detail & Related papers (2022-06-01T09:20:30Z) - Application of Transfer Learning and Ensemble Learning in Image-level
Classification for Breast Histopathology [9.037868656840736]
In Computer-Aided Diagnosis (CAD), traditional classification models mostly use a single network to extract features.
This paper proposes a deep ensemble model based on image-level labels for the binary classification of benign and malignant lesions.
Result: In the ensemble network model with accuracy as the weight, the image-level binary classification achieves an accuracy of $98.90%$.
arXiv Detail & Related papers (2022-04-18T13:31:53Z) - Intelligent Masking: Deep Q-Learning for Context Encoding in Medical
Image Analysis [48.02011627390706]
We develop a novel self-supervised approach that occludes targeted regions to improve the pre-training procedure.
We show that training the agent against the prediction model can significantly improve the semantic features extracted for downstream classification tasks.
arXiv Detail & Related papers (2022-03-25T19:05:06Z) - Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for
Thoracic Disease Identification [83.6017225363714]
deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance.
For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming.
In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
arXiv Detail & Related papers (2021-02-26T02:29:30Z) - Big Self-Supervised Models Advance Medical Image Classification [36.23989703428874]
We study the effectiveness of self-supervised learning as a pretraining strategy for medical image classification.
We use a novel Multi-Instance Contrastive Learning (MICLe) method that uses multiple images of the underlying pathology per patient case.
We show that big self-supervised models are robust to distribution shift and can learn efficiently with a small number of labeled medical images.
arXiv Detail & Related papers (2021-01-13T17:36:31Z) - Analysis of skin lesion images with deep learning [0.0]
We evaluate the current state of the art in the classification of dermoscopic images.
Various deep neural network architectures pre-trained on the ImageNet data set are adapted to a combined training data set.
The performance and applicability of these models for the detection of eight classes of skin lesions are examined.
arXiv Detail & Related papers (2021-01-11T10:58:36Z) - Multi-label Thoracic Disease Image Classification with Cross-Attention
Networks [65.37531731899837]
We propose a novel scheme of Cross-Attention Networks (CAN) for automated thoracic disease classification from chest x-ray images.
We also design a new loss function that beyond cross-entropy loss to help cross-attention process and is able to overcome the imbalance between classes and easy-dominated samples within each class.
arXiv Detail & Related papers (2020-07-21T14:37:00Z) - Semi-Supervised Cervical Dysplasia Classification With Learnable Graph
Convolutional Network [25.685255609487623]
Digital cervicography has great potential as a primary or auxiliary screening tool.
Traditional fully-supervised training of such systems requires large amounts of annotated data.
We propose a novel graph convolutional network (GCN) based semi-supervised classification model.
arXiv Detail & Related papers (2020-04-01T01:53:26Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.