The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis

Recently, deep learning frameworks have rapidly become the main methodology for analyzing medical images. Due to their powerful learning ability and advantages in dealing with complex patterns, deep learning algorithms are ideal for image analysis challenges, particularly in the field of digital pathology. The variety of image analysis tasks in the context of deep learning includes classification (e.g., healthy vs. cancerous tissue), detection (e.g., lymphocytes and mitosis counting), and segmentation (e.g., nuclei and glands segmentation). The majority of recent machine learning methods in digital pathology have a pre- and/or post-processing stage which is integrated with a deep neural network. These stages, based on traditional image processing methods, are employed to make the subsequent classification, detection, or segmentation problem easier to solve. Several studies have shown how the integration of pre- and post-processing methods within a deep learning pipeline can further increase the model's performance when compared to the network by itself. The aim of this review is to provide an overview on the types of methods that are used within deep learning frameworks either to optimally prepare the input (pre-processing) or to improve the results of the network output (post-processing), focusing on digital pathology image analysis. Many of the techniques presented here, especially the post-processing methods, are not limited to digital pathology but can be extended to almost any image analysis field

The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis.

Recently, deep learning frameworks have rapidly become the main methodology for analyzing medical images. Due to their powerful learning ability and advantages in dealing with complex patterns, deep learning algorithms are ideal for image analysis challenges, particularly in the field of digital pathology. The variety of image analysis tasks in the context of deep learning includes classification (e.g., healthy vs. cancerous tissue), detection (e.g., lymphocytes and mitosis counting), and segmentation (e.g., nuclei and glands segmentation). The majority of recent machine learning methods in digital pathology have a pre- and/or post-processing stage which is integrated with a deep neural network. These stages, based on traditional image processing methods, are employed to make the subsequent classification, detection, or segmentation problem easier to solve. Several studies have shown how the integration of pre- and post-processing methods within a deep learning pipeline can further increase the model's performance when compared to the network by itself. The aim of this review is to provide an overview on the types of methods that are used within deep learning frameworks either to optimally prepare the input (pre-processing) or to improve the results of the network output (post-processing), focusing on digital pathology image analysis. Many of the techniques presented here, especially the post-processing methods, are not limited to digital pathology but can be extended to almost any image analysis field

Machine Learning Models for Deciphering Regulatory Mechanisms and Morphological Variations in Cancer

The exponential growth of multi-omics biological datasets is resulting in an emerging paradigm shift in fundamental biological research. In recent years, imaging and transcriptomics datasets are increasingly incorporated into biological studies, pushing biology further into the domain of data-intensive-sciences. New approaches and tools from statistics, computer science, and data engineering are profoundly influencing biological research. Harnessing this ever-growing deluge of multi-omics biological data requires the development of novel and creative computational approaches. In parallel, fundamental research in data sciences and Artificial Intelligence (AI) has advanced tremendously, allowing the scientific community to generate a massive amount of knowledge from data. Advances in Deep Learning (DL), in particular, are transforming many branches of engineering, science, and technology. Several of these methodologies have already been adapted for harnessing biological datasets; however, there is still a need to further adapt and tailor these techniques to new and emerging technologies. In this dissertation, we present computational algorithms and tools that we have developed to study gene-regulation and cellular morphology in cancer. The models and platforms that we have developed are general and widely applicable to several problems relating to dysregulation of gene expression in diseases. Our pipelines and software packages are disseminated in public repositories for larger scientific community use. This dissertation is organized in three main projects. In the first project, we present Causal Inference Engine (CIE), an integrated platform for the identification and interpretation of active regulators of transcriptional response. The platform offers visualization tools and pathway enrichment analysis to map predicted regulators to Reactome pathways. We provide a parallelized R-package for fast and flexible directional enrichment analysis to run the inference on custom regulatory networks. Next, we designed and developed MODEX, a fully automated text-mining system to extract and annotate causal regulatory interaction between Transcription Factors (TFs) and genes from the biomedical literature. MODEX uses putative TF-gene interactions derived from high-throughput ChIP-Seq or other experiments and seeks to collect evidence and meta-data in the biomedical literature to validate and annotate the interactions. MODEX is a complementary platform to CIE that provides auxiliary information on CIE inferred interactions by mining the literature. In the second project, we present a Convolutional Neural Network (CNN) classifier to perform a pan-cancer analysis of tumor morphology, and predict mutations in key genes. The main challenges were to determine morphological features underlying a genetic status and assess whether these features were common in other cancer types. We trained an Inception-v3 based model to predict TP53 mutation in five cancer types with the highest rate of TP53 mutations. We also performed a cross-classification analysis to assess shared morphological features across multiple cancer types. Further, we applied a similar methodology to classify HER2 status in breast cancer and predict response to treatment in HER2 positive samples. For this study, our training slides were manually annotated by expert pathologists to highlight Regions of Interest (ROIs) associated with HER2+/- tumor microenvironment. Our results indicated that there are strong morphological features associated with each tumor type. Moreover, our predictions highly agree with manual annotations in the test set, indicating the feasibility of our approach in devising an image-based diagnostic tool for HER2 status and treatment response prediction. We have validated our model using samples from an independent cohort, which demonstrates the generalizability of our approach. Finally, in the third project, we present an approach to use spatial transcriptomics data to predict spatially-resolved active gene regulatory mechanisms in tissues. Using spatial transcriptomics, we identified tissue regions with differentially expressed genes and applied our CIE methodology to predict active TFs that can potentially regulate the marker genes in the region. This project bridged the gap between inference of active regulators using molecular data and morphological studies using images. The results demonstrate a significant local pattern in TF activity across the tissue, indicating differential spatial-regulation in tissues. The results suggest that the integrative analysis of spatial transcriptomics data with CIE can capture discriminant features and identify localized TF-target links in the tissue

Explainable Histopathology Image Classification with Self-organizing Maps: A Granular Computing Perspective

The automatic analysis of histology images is an open research field where machine learning techniques and neural networks, especially deep architectures, are considered successful tools due to their abilities in image classification. This paper proposes a granular computing methodology for histopathological image classification. It is based on embedding tiles of histopathology images using deep metric learning, where a self-organizing map is adopted to generate the granular structure in this learned embedding space. The SOM enables the implementation of an explainable mechanism by visualizing a knowledge space that the experts can use to analyze and classify the new images. Additionally, it provides confidence in the classification results while highlighting each important image fragment, with the benefit of reducing the number of false negatives. An exemplary case is when an image detail is indicated, with small confidence, as malignant in an image globally classified as benign. Another implemented feature is the proposal of additional labelled image tiles sharing the same characteristics to specify the context of the output decision. The proposed system was tested using three histopathology image datasets, obtaining the accuracy of the state-of-the-art black-box methods based on deep learning neural networks. Differently from the methodologies proposed so far for the same purpose, this paper introduces a novel explainable method for medical image analysis where the advantages of the deep learning neural networks used to build the embedding space for the image tiles are combined with the intrinsic explainability of the granular process obtained using the clustering property of a selforganizing map

The Artificial Intelligence in Digital Pathology and Digital Radiology: Where Are We?

This book is a reprint of the Special Issue entitled "The Artificial Intelligence in Digital Pathology and Digital Radiology: Where Are We?". Artificial intelligence is extending into the world of both digital radiology and digital pathology, and involves many scholars in the areas of biomedicine, technology, and bioethics. There is a particular need for scholars to focus on both the innovations in this field and the problems hampering integration into a robust and effective process in stable health care models in the health domain. Many professionals involved in these fields of digital health were encouraged to contribute with their experiences. This book contains contributions from various experts across different fields. Aspects of the integration in the health domain have been faced. Particular space was dedicated to overviewing the challenges, opportunities, and problems in both radiology and pathology. Clinal deepens are available in cardiology, the hystopathology of breast cancer, and colonoscopy. Dedicated studies were based on surveys which investigated students and insiders, opinions, attitudes, and self-perception on the integration of artificial intelligence in this field

Computational Pathology: A Survey Review and The Way Forward

Computational Pathology CPath is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath is to develop infrastructure and workflows of digital diagnostics as an assistive CAD system for clinical pathology, facilitating transformational changes in the diagnosis and treatment of cancer that are mainly address by CPath tools. With evergrowing developments in deep learning and computer vision algorithms, and the ease of the data flow from digital pathology, currently CPath is witnessing a paradigm shift. Despite the sheer volume of engineering and scientific works being introduced for cancer image analysis, there is still a considerable gap of adopting and integrating these algorithms in clinical practice. This raises a significant question regarding the direction and trends that are undertaken in CPath. In this article we provide a comprehensive review of more than 800 papers to address the challenges faced in problem design all-the-way to the application and implementation viewpoints. We have catalogued each paper into a model-card by examining the key works and challenges faced to layout the current landscape in CPath. We hope this helps the community to locate relevant works and facilitate understanding of the field's future directions. In a nutshell, we oversee the CPath developments in cycle of stages which are required to be cohesively linked together to address the challenges associated with such multidisciplinary science. We overview this cycle from different perspectives of data-centric, model-centric, and application-centric problems. We finally sketch remaining challenges and provide directions for future technical developments and clinical integration of CPath (https://github.com/AtlasAnalyticsLab/CPath_Survey).Comment: Accepted in Elsevier Journal of Pathology Informatics (JPI) 202

Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A review

Cancer remains one of the most challenging diseases to treat in the medical field. Machine learning has enabled in-depth analysis of rich multi-omics profiles and medical imaging for cancer diagnosis and prognosis. Despite these advancements, machine learning models face challenges stemming from limited labeled sample sizes, the intricate interplay of high-dimensionality data types, the inherent heterogeneity observed among patients and within tumors, and concerns about interpretability and consistency with existing biomedical knowledge. One approach to surmount these challenges is to integrate biomedical knowledge into data-driven models, which has proven potential to improve the accuracy, robustness, and interpretability of model results. Here, we review the state-of-the-art machine learning studies that adopted the fusion of biomedical knowledge and data, termed knowledge-informed machine learning, for cancer diagnosis and prognosis. Emphasizing the properties inherent in four primary data types including clinical, imaging, molecular, and treatment data, we highlight modeling considerations relevant to these contexts. We provide an overview of diverse forms of knowledge representation and current strategies of knowledge integration into machine learning pipelines with concrete examples. We conclude the review article by discussing future directions to advance cancer research through knowledge-informed machine learning.Comment: 41 pages, 4 figures, 2 table