186 research outputs found
Histopathological image analysis : a review
Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe
The Whole Pathological Slide Classification via Weakly Supervised Learning
Due to its superior efficiency in utilizing annotations and addressing
gigapixel-sized images, multiple instance learning (MIL) has shown great
promise as a framework for whole slide image (WSI) classification in digital
pathology diagnosis. However, existing methods tend to focus on advanced
aggregators with different structures, often overlooking the intrinsic features
of H\&E pathological slides. To address this limitation, we introduced two
pathological priors: nuclear heterogeneity of diseased cells and spatial
correlation of pathological tiles. Leveraging the former, we proposed a data
augmentation method that utilizes stain separation during extractor training
via a contrastive learning strategy to obtain instance-level representations.
We then described the spatial relationships between the tiles using an
adjacency matrix. By integrating these two views, we designed a multi-instance
framework for analyzing H\&E-stained tissue images based on pathological
inductive bias, encompassing feature extraction, filtering, and aggregation.
Extensive experiments on the Camelyon16 breast dataset and TCGA-NSCLC Lung
dataset demonstrate that our proposed framework can effectively handle tasks
related to cancer detection and differentiation of subtypes, outperforming
state-of-the-art medical image classification methods based on MIL. The code
will be released later
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
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
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
Artificial Intelligence for Digital and Computational Pathology
Advances in digitizing tissue slides and the fast-paced progress in
artificial intelligence, including deep learning, have boosted the field of
computational pathology. This field holds tremendous potential to automate
clinical diagnosis, predict patient prognosis and response to therapy, and
discover new morphological biomarkers from tissue images. Some of these
artificial intelligence-based systems are now getting approved to assist
clinical diagnosis; however, technical barriers remain for their widespread
clinical adoption and integration as a research tool. This Review consolidates
recent methodological advances in computational pathology for predicting
clinical end points in whole-slide images and highlights how these developments
enable the automation of clinical practice and the discovery of new biomarkers.
We then provide future perspectives as the field expands into a broader range
of clinical and research tasks with increasingly diverse modalities of clinical
data
Quilt-1M: One Million Image-Text Pairs for Histopathology
Recent accelerations in multi-modal applications have been made possible with
the plethora of image and text data available online. However, the scarcity of
analogous data in the medical field, specifically in histopathology, has halted
comparable progress. To enable similar representation learning for
histopathology, we turn to YouTube, an untapped resource of videos, offering
hours of valuable educational histopathology videos from expert
clinicians. From YouTube, we curate Quilt: a large-scale vision-language
dataset consisting of image and text pairs. Quilt was automatically
curated using a mixture of models, including large language models, handcrafted
algorithms, human knowledge databases, and automatic speech recognition. In
comparison, the most comprehensive datasets curated for histopathology amass
only around K samples. We combine Quilt with datasets from other sources,
including Twitter, research papers, and the internet in general, to create an
even larger dataset: Quilt-1M, with M paired image-text samples, marking it
as the largest vision-language histopathology dataset to date. We demonstrate
the value of Quilt-1M by fine-tuning a pre-trained CLIP model. Our model
outperforms state-of-the-art models on both zero-shot and linear probing tasks
for classifying new histopathology images across diverse patch-level
datasets of different sub-pathologies and cross-modal retrieval tasks
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
Analysis of Signal Decomposition and Stain Separation methods for biomedical applications
Nowadays, the biomedical signal processing and classification and medical image interpretation play an essential role in the detection and diagnosis of several human diseases. The problem of high variability and heterogeneity of information, which is extracted from digital data, can be addressed with signal decomposition and stain separation techniques which can be useful approaches to highlight hidden patterns or rhythms in biological signals and specific cellular structures in histological color images, respectively. This thesis work can be divided into two macro-sections. In the first part (Part I), a novel cascaded RNN model based on long short-term memory (LSTM) blocks is presented with the aim to classify sleep stages automatically. A general workflow based on single-channel EEG signals is developed to enhance the low performance in staging N1 sleep without reducing the performances in the other sleep stages (i.e. Wake, N2, N3 and REM). In the same context, several signal decomposition techniques and time-frequency representations are deployed for the analysis of EEG signals. All extracted features are analyzed by using a novel correlation-based timestep feature selection and finally the selected features are fed to a bidirectional RNN model. In the second part (Part II), a fully automated method named SCAN (Stain Color Adaptive Normalization) is proposed for the separation and normalization of staining in digital pathology. This normalization system allows to standardize digitally, automatically and in a few seconds, the color intensity of a tissue slide with respect to that of a target image, in order to improve the pathologist’s diagnosis and increase the accuracy of computer-assisted diagnosis (CAD) systems. Multiscale evaluation and multi-tissue comparison are performed for assessing the robustness of the proposed method. In addition, a stain normalization based on a novel mathematical technique, named ICD (Inverse Color Deconvolution) is developed for immunohistochemical (IHC) staining in histopathological images. In conclusion, the proposed techniques achieve satisfactory results compared to state-of-the-art methods in the same research field. The workflow proposed in this thesis work and the developed algorithms can be employed for the analysis and interpretation of other biomedical signals and for digital medical image analysis
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