8 research outputs found
Weakly-supervised High-resolution Segmentation of Mammography Images for Breast Cancer Diagnosis
In the last few years, deep learning classifiers have shown promising results
in image-based medical diagnosis. However, interpreting the outputs of these
models remains a challenge. In cancer diagnosis, interpretability can be
achieved by localizing the region of the input image responsible for the
output, i.e. the location of a lesion. Alternatively, segmentation or detection
models can be trained with pixel-wise annotations indicating the locations of
malignant lesions. Unfortunately, acquiring such labels is labor-intensive and
requires medical expertise. To overcome this difficulty, weakly-supervised
localization can be utilized. These methods allow neural network classifiers to
output saliency maps highlighting the regions of the input most relevant to the
classification task (e.g. malignant lesions in mammograms) using only
image-level labels (e.g. whether the patient has cancer or not) during
training. When applied to high-resolution images, existing methods produce
low-resolution saliency maps. This is problematic in applications in which
suspicious lesions are small in relation to the image size. In this work, we
introduce a novel neural network architecture to perform weakly-supervised
segmentation of high-resolution images. The proposed model selects regions of
interest via coarse-level localization, and then performs fine-grained
segmentation of those regions. We apply this model to breast cancer diagnosis
with screening mammography, and validate it on a large clinically-realistic
dataset. Measured by Dice similarity score, our approach outperforms existing
methods by a large margin in terms of localization performance of benign and
malignant lesions, relatively improving the performance by 39.6% and 20.0%,
respectively. Code and the weights of some of the models are available at
https://github.com/nyukat/GLAMComment: The last two authors contributed equally. Accepted to Medical Imaging
with Deep Learning (MIDL) 202
Explainable artificial intelligence (XAI) in deep learning-based medical image analysis
With an increase in deep learning-based methods, the call for explainability
of such methods grows, especially in high-stakes decision making areas such as
medical image analysis. This survey presents an overview of eXplainable
Artificial Intelligence (XAI) used in deep learning-based medical image
analysis. A framework of XAI criteria is introduced to classify deep
learning-based medical image analysis methods. Papers on XAI techniques in
medical image analysis are then surveyed and categorized according to the
framework and according to anatomical location. The paper concludes with an
outlook of future opportunities for XAI in medical image analysis.Comment: Submitted for publication. Comments welcome by email to first autho
Deep weakly-supervised learning methods for classification and localization in histology images: a survey
Using state-of-the-art deep learning models for cancer diagnosis presents
several challenges related to the nature and availability of labeled histology
images. In particular, cancer grading and localization in these images normally
relies on both image- and pixel-level labels, the latter requiring a costly
annotation process. In this survey, deep weakly-supervised learning (WSL)
models are investigated to identify and locate diseases in histology images,
without the need for pixel-level annotations. Given training data with global
image-level labels, these models allow to simultaneously classify histology
images and yield pixel-wise localization scores, thereby identifying the
corresponding regions of interest (ROI). Since relevant WSL models have mainly
been investigated within the computer vision community, and validated on
natural scene images, we assess the extent to which they apply to histology
images which have challenging properties, e.g. very large size, similarity
between foreground/background, highly unstructured regions, stain
heterogeneity, and noisy/ambiguous labels. The most relevant models for deep
WSL are compared experimentally in terms of accuracy (classification and
pixel-wise localization) on several public benchmark histology datasets for
breast and colon cancer -- BACH ICIAR 2018, BreaKHis, CAMELYON16, and GlaS.
Furthermore, for large-scale evaluation of WSL models on histology images, we
propose a protocol to construct WSL datasets from Whole Slide Imaging. Results
indicate that several deep learning models can provide a high level of
classification accuracy, although accurate pixel-wise localization of cancer
regions remains an issue for such images. Code is publicly available.Comment: 35 pages, 18 figure
The Effectiveness of Transfer Learning Systems on Medical Images
Deep neural networks have revolutionized the performances of many machine learning tasks such as medical image classification and segmentation. Current deep learning (DL) algorithms, specifically convolutional neural networks are increasingly becoming the methodological choice for most medical image analysis. However, training these deep neural networks requires high computational resources and very large amounts of labeled data which is often expensive and laborious. Meanwhile, recent studies have shown the transfer learning (TL) paradigm as an attractive choice in providing promising solutions to challenges of shortage in the availability of labeled medical images. Accordingly, TL enables us to leverage the knowledge learned from related data to solve a new problem.
The objective of this dissertation is to examine the effectiveness of TL systems on medical images. First, a comprehensive systematic literature review was performed to provide an up-to-date status of TL systems on medical images. Specifically, we proposed a novel conceptual framework to organize the review. Second, a novel DL network was pretrained on natural images and utilized to evaluate the effectiveness of TL on a very large medical image dataset, specifically Chest X-rays images. Lastly, domain adaptation using an autoencoder was evaluated on the medical image dataset and the results confirmed the effectiveness of TL through fine-tuning strategies.
We make several contributions to TL systems on medical image analysis: Firstly, we present a novel survey of TL on medical images and propose a new conceptual framework to organize the findings. Secondly, we propose a novel DL architecture to improve learned representations of medical images while mitigating the problem of vanishing gradients. Additionally, we identified the optimal cut-off layer (OCL) that provided the best model performance. We found that the higher layers in the proposed deep model give a better feature representation of our medical image task. Finally, we analyzed the effect of domain adaptation by fine-tuning an autoencoder on our medical images and provide theoretical contributions on the application of the transductive TL approach. The contributions herein reveal several research gaps to motivate future research and contribute to the body of literature in this active research area of TL systems on medical image analysis