20 research outputs found
Edge-variational Graph Convolutional Networks for Uncertainty-aware Disease Prediction
There is a rising need for computational models that can complementarily
leverage data of different modalities while investigating associations between
subjects for population-based disease analysis. Despite the success of
convolutional neural networks in representation learning for imaging data, it
is still a very challenging task. In this paper, we propose a generalizable
framework that can automatically integrate imaging data with non-imaging data
in populations for uncertainty-aware disease prediction. At its core is a
learnable adaptive population graph with variational edges, which we
mathematically prove that it is optimizable in conjunction with graph
convolutional neural networks. To estimate the predictive uncertainty related
to the graph topology, we propose the novel concept of Monte-Carlo edge
dropout. Experimental results on four databases show that our method can
consistently and significantly improve the diagnostic accuracy for Autism
spectrum disorder, Alzheimer's disease, and ocular diseases, indicating its
generalizability in leveraging multimodal data for computer-aided diagnosis.Comment: Accepted to MICCAI 202
Cancer diagnosis using deep learning: A bibliographic review
In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements
Going Deep in Medical Image Analysis: Concepts, Methods, Challenges and Future Directions
Medical Image Analysis is currently experiencing a paradigm shift due to Deep
Learning. This technology has recently attracted so much interest of the
Medical Imaging community that it led to a specialized conference in `Medical
Imaging with Deep Learning' in the year 2018. This article surveys the recent
developments in this direction, and provides a critical review of the related
major aspects. We organize the reviewed literature according to the underlying
Pattern Recognition tasks, and further sub-categorize it following a taxonomy
based on human anatomy. This article does not assume prior knowledge of Deep
Learning and makes a significant contribution in explaining the core Deep
Learning concepts to the non-experts in the Medical community. Unique to this
study is the Computer Vision/Machine Learning perspective taken on the advances
of Deep Learning in Medical Imaging. This enables us to single out `lack of
appropriately annotated large-scale datasets' as the core challenge (among
other challenges) in this research direction. We draw on the insights from the
sister research fields of Computer Vision, Pattern Recognition and Machine
Learning etc.; where the techniques of dealing with such challenges have
already matured, to provide promising directions for the Medical Imaging
community to fully harness Deep Learning in the future
Domain Generalization in Computational Pathology: Survey and Guidelines
Deep learning models have exhibited exceptional effectiveness in
Computational Pathology (CPath) by tackling intricate tasks across an array of
histology image analysis applications. Nevertheless, the presence of
out-of-distribution data (stemming from a multitude of sources such as
disparate imaging devices and diverse tissue preparation methods) can cause
\emph{domain shift} (DS). DS decreases the generalization of trained models to
unseen datasets with slightly different data distributions, prompting the need
for innovative \emph{domain generalization} (DG) solutions. Recognizing the
potential of DG methods to significantly influence diagnostic and prognostic
models in cancer studies and clinical practice, we present this survey along
with guidelines on achieving DG in CPath. We rigorously define various DS
types, systematically review and categorize existing DG approaches and
resources in CPath, and provide insights into their advantages, limitations,
and applicability. We also conduct thorough benchmarking experiments with 28
cutting-edge DG algorithms to address a complex DG problem. Our findings
suggest that careful experiment design and CPath-specific Stain Augmentation
technique can be very effective. However, there is no one-size-fits-all
solution for DG in CPath. Therefore, we establish clear guidelines for
detecting and managing DS depending on different scenarios. While most of the
concepts, guidelines, and recommendations are given for applications in CPath,
we believe that they are applicable to most medical image analysis tasks as
well.Comment: Extended Versio
Deep Learning in Medical Image Analysis
The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis
Learning Discriminative Representations for Gigapixel Images
Digital images of tumor tissue are important diagnostic and prognostic tools for pathologists. Recent advancement in digital pathology has led to an abundance of digitized histopathology slides, called whole-slide images. Computational analysis of whole-slide images is a challenging task as they are generally gigapixel files, often one or more gigabytes in size. However, these computational methods provide a unique opportunity to improve the objectivity and accuracy of diagnostic interpretations in histopathology. Recently, deep learning has been successful in characterizing images for vision-based applications in multiple domains. But its applications are relatively less explored in the histopathology domain mostly due to the following two challenges. Firstly, there is difficulty in scaling deep learning methods for processing large gigapixel histopathology images. Secondly, there is a lack of diversified and labeled datasets due to privacy constraints as well as workflow and technical challenges in the healthcare sector. The main goal of this dissertation is to explore and develop deep models to learn discriminative representations of whole slide images while overcoming the existing challenges. A three-staged approach was considered in this research. In the first stage, a framework called Yottixel is proposed. It represents a whole-slide image as a set of multiple representative patches, called mosaic. The mosaic enables convenient processing and compact representation of an entire high-resolution whole-slide image. Yottixel allows faster retrieval of similar whole-slide images within large archives of digital histopathology images. Such retrieval technology enables pathologists to tap into the past diagnostic data on demand. Yottixel is validated on the largest public archive of whole-slide images (The Cancer Genomic Atlas), achieving promising results. Yottixel is an unsupervised method that limits its performance on specific tasks especially when the labeled (or partially labeled) dataset can be available. In the second stage, multi-instance learning (MIL) is used to enhance the cancer subtype prediction through weakly-supervised training. Three MIL methods have been proposed, each improving upon the previous one. The first one is based on memory-based models, the second uses attention-based models, and the third one uses graph neural networks. All three methods are incorporated in Yottixel to classify entire whole-slide images with no pixel-level annotations. Access to large-scale and diversified datasets is a primary driver of the advancement and adoption of machine learning technologies. However, healthcare has many restrictive rules around data sharing, limiting research and model development. In the final stage, a federated learning scheme called ProxyFL is developed that enables collaborative training of Yottixel among the multiple healthcare organizations without centralization of the sensitive medical data. The combined research in all the three stages of the Ph.D. has resulted in the development of a holistic and practical framework for learning discriminative and compact representations of whole-slide images in digital pathology