72 research outputs found
Mitosis Detection, Fast and Slow: Robust and Efficient Detection of Mitotic Figures
Counting of mitotic figures is a fundamental step in grading and
prognostication of several cancers. However, manual mitosis counting is tedious
and time-consuming. In addition, variation in the appearance of mitotic figures
causes a high degree of discordance among pathologists. With advances in deep
learning models, several automatic mitosis detection algorithms have been
proposed but they are sensitive to {\em domain shift} often seen in histology
images. We propose a robust and efficient two-stage mitosis detection
framework, which comprises mitosis candidate segmentation ({\em Detecting
Fast}) and candidate refinement ({\em Detecting Slow}) stages. The proposed
candidate segmentation model, termed \textit{EUNet}, is fast and accurate due
to its architectural design. EUNet can precisely segment candidates at a lower
resolution to considerably speed up candidate detection. Candidates are then
refined using a deeper classifier network, EfficientNet-B7, in the second
stage. We make sure both stages are robust against domain shift by
incorporating domain generalization methods. We demonstrate state-of-the-art
performance and generalizability of the proposed model on the three largest
publicly available mitosis datasets, winning the two mitosis domain
generalization challenge contests (MIDOG21 and MIDOG22). Finally, we showcase
the utility of the proposed algorithm by processing the TCGA breast cancer
cohort (1,125 whole-slide images) to generate and release a repository of more
than 620K mitotic figures.Comment: Extended version of the work done for MIDOG challenge submissio
Stain Consistency Learning: Handling Stain Variation for Automatic Digital Pathology Segmentation
Stain variation is a unique challenge associated with automated analysis of
digital pathology. Numerous methods have been developed to improve the
robustness of machine learning methods to stain variation, but comparative
studies have demonstrated limited benefits to performance. Moreover, methods to
handle stain variation were largely developed for H&E stained data, with
evaluation generally limited to classification tasks. Here we propose Stain
Consistency Learning, a novel framework combining stain-specific augmentation
with a stain consistency loss function to learn stain colour invariant
features. We perform the first, extensive comparison of methods to handle stain
variation for segmentation tasks, comparing ten methods on Masson's trichrome
and H&E stained cell and nuclei datasets, respectively. We observed that stain
normalisation methods resulted in equivalent or worse performance, while stain
augmentation or stain adversarial methods demonstrated improved performance,
with the best performance consistently achieved by our proposed approach. The
code is available at: https://github.com/mlyg/stain_consistency_learnin
Analysis of Cellular and Subcellular Morphology using Machine Learning in Microscopy Images
Human cells undergo various morphological changes due to progression in the cell-cycle or environmental factors. Classification of these morphological states is vital for effective clinical decisions. Automated classification systems based on machine learning models are data-driven and efficient and help to avoid subjective outcomes. However, the efficacy of these models is highly dependent on the feature description along with the amount and nature of the training data.
This thesis presents three studies of automated image-based classification of cellular and subcellular morphologies. The first study presents 3D Sorted Random Projections (SRP) which includes the proposed approach to compute 3D plane information for texture description of 3D nuclear images. The proposed 3D SRP is used to classify nuclear morphology and measure changes in heterochromatin, which in turn helps to characterise cellular states. Classification performance evaluated on 3D images of the human fibroblast and prostate cancer cell lines shows that 3D SRP provides better classification than other feature descriptors.
The second study is on imbalanced multiclass and single-label classification of blood cell images. The scarcity of minority sam ples causes a drop in classification performance on minority classes. This study proposes oversampling of minority samples us ing data augmentation approaches, namely mixup, WGAN-div and novel nonlinear mixup, along with a minority class focussed sampling strategy. Classification performance evaluated using F1-score shows that the proposed deep learning framework out performs state-of-the art approaches on publicly available images of human T-lymphocyte cells and red blood cells.
The third study is on protein subcellular localisation, which is an imbalanced multiclass and multilabel classification problem. In order to handle data imbalance, this study proposes an oversampling method which includes synthetic images constructed using nonlinear mixup and geometric/colour transformations. The regularisation capability of nonlinear mixup is further improved for protein images. In addition, an imbalance aware sampling strategy is proposed to identify minority and medium classes in the dataset and include them during training. Classification performance evaluated on the Human Protein Atlas Kaggle challenge dataset using F1-score shows that the proposed deep learning framework achieves better predictions than existing methods
Histopathological Image Analysis with Style-Augmented Feature Domain Mixing for Improved Generalization
Histopathological images are essential for medical diagnosis and treatment
planning, but interpreting them accurately using machine learning can be
challenging due to variations in tissue preparation, staining and imaging
protocols. Domain generalization aims to address such limitations by enabling
the learning models to generalize to new datasets or populations. Style
transfer-based data augmentation is an emerging technique that can be used to
improve the generalizability of machine learning models for histopathological
images. However, existing style transfer-based methods can be computationally
expensive, and they rely on artistic styles, which can negatively impact model
accuracy. In this study, we propose a feature domain style mixing technique
that uses adaptive instance normalization to generate style-augmented versions
of images. We compare our proposed method with existing style transfer-based
data augmentation methods and found that it performs similarly or better,
despite requiring less computation and time. Our results demonstrate the
potential of feature domain statistics mixing in the generalization of learning
models for histopathological image analysis.Comment: Paper is published in MedAGI 2023 (MICCAI 2023 1st International
Workshop on Foundation Models for General Medical AI) Code link:
https://github.com/Vaibhav-Khamankar/FuseStyle Paper link:
https://nbviewer.org/github/MedAGI/medagi.github.io/blob/main/src/assets/papers/P17.pd
A Comprehensive Overview of Computational Nuclei Segmentation Methods in Digital Pathology
In the cancer diagnosis pipeline, digital pathology plays an instrumental
role in the identification, staging, and grading of malignant areas on biopsy
tissue specimens. High resolution histology images are subject to high variance
in appearance, sourcing either from the acquisition devices or the H\&E
staining process. Nuclei segmentation is an important task, as it detects the
nuclei cells over background tissue and gives rise to the topology, size, and
count of nuclei which are determinant factors for cancer detection. Yet, it is
a fairly time consuming task for pathologists, with reportedly high
subjectivity. Computer Aided Diagnosis (CAD) tools empowered by modern
Artificial Intelligence (AI) models enable the automation of nuclei
segmentation. This can reduce the subjectivity in analysis and reading time.
This paper provides an extensive review, beginning from earlier works use
traditional image processing techniques and reaching up to modern approaches
following the Deep Learning (DL) paradigm. Our review also focuses on the weak
supervision aspect of the problem, motivated by the fact that annotated data is
scarce. At the end, the advantages of different models and types of supervision
are thoroughly discussed. Furthermore, we try to extrapolate and envision how
future research lines will potentially be, so as to minimize the need for
labeled data while maintaining high performance. Future methods should
emphasize efficient and explainable models with a transparent underlying
process so that physicians can trust their output.Comment: 47 pages, 27 figures, 9 table
Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions
Breast cancer has reached the highest incidence rate worldwide among all
malignancies since 2020. Breast imaging plays a significant role in early
diagnosis and intervention to improve the outcome of breast cancer patients. In
the past decade, deep learning has shown remarkable progress in breast cancer
imaging analysis, holding great promise in interpreting the rich information
and complex context of breast imaging modalities. Considering the rapid
improvement in the deep learning technology and the increasing severity of
breast cancer, it is critical to summarize past progress and identify future
challenges to be addressed. In this paper, we provide an extensive survey of
deep learning-based breast cancer imaging research, covering studies on
mammogram, ultrasound, magnetic resonance imaging, and digital pathology images
over the past decade. The major deep learning methods, publicly available
datasets, and applications on imaging-based screening, diagnosis, treatment
response prediction, and prognosis are described in detail. Drawn from the
findings of this survey, we present a comprehensive discussion of the
challenges and potential avenues for future research in deep learning-based
breast cancer imaging.Comment: Survey, 41 page
Out-of-Distribution Generalization of Gigapixel Image Representation
This thesis addresses the significant challenge of improving the generalization capabilities of artificial deep neural networks in the classification of whole slide images (WSIs) in histopathology across different and unseen hospitals. It is a critical issue in AI applications for vision-based healthcare tasks, given that current standard methodologies struggle with out-of-distribution (OOD) data from varying hospital sources. In histopathology, distribution shifts can arise due to image acquisition variances across different scanner vendors, differences in laboratory routines and staining procedures, and diversity in patient demographics. This work investigates two critical forms of generalization within histopathology: magnification generalization and OOD generalization towards different hospitals. One chapter of this thesis is dedicated to the exploration of magnification generalization, acknowledging the variability in histopathological images due to distinct magnification levels and seeking to enhance the model's robustness by learning invariant features across these levels. However, the major part of this work focuses on OOD generalization, specifically unseen hospital data. The objective is to leverage knowledge encapsulated in pre-existing models to help new models adapt to diverse data scenarios and ensure their efficient operation in different hospital environments. Additionally, the concept of Hospital-Agnostic (HA) learning regimes is introduced, focusing on invariant characteristics across hospitals and aiming to establish a learning model that sustains stable performance in varied hospital settings. The culmination of this research introduces a comprehensive method, termed ALFA (Exploiting All Levels of Feature Abstraction), that not only considers invariant features across hospitals but also extracts a broader set of features from input images, thus maximizing the model's generalization potential. The findings of this research are expected to have significant implications for the deployment of medical image classification systems using deep models in clinical settings. The proposed methods allow for more accurate and reliable diagnostic support across various hospital environments, thereby improving diagnostic accuracy and reliability, and paving the way for enhanced generalization in histopathology diagnostics using deep learning techniques. Future research directions may build on expanding these investigations to further improve generalization in histopathology
Domain Generalization for Medical Image Analysis: A Survey
Medical Image Analysis (MedIA) has become an essential tool in medicine and
healthcare, aiding in disease diagnosis, prognosis, and treatment planning, and
recent successes in deep learning (DL) have made significant contributions to
its advances. However, DL models for MedIA remain challenging to deploy in
real-world situations, failing for generalization under the distributional gap
between training and testing samples, known as a distribution shift problem.
Researchers have dedicated their efforts to developing various DL methods to
adapt and perform robustly on unknown and out-of-distribution data
distributions. This paper comprehensively reviews domain generalization studies
specifically tailored for MedIA. We provide a holistic view of how domain
generalization techniques interact within the broader MedIA system, going
beyond methodologies to consider the operational implications on the entire
MedIA workflow. Specifically, we categorize domain generalization methods into
data-level, feature-level, model-level, and analysis-level methods. We show how
those methods can be used in various stages of the MedIA workflow with DL
equipped from data acquisition to model prediction and analysis. Furthermore,
we include benchmark datasets and applications used to evaluate these
approaches and analyze the strengths and weaknesses of various methods,
unveiling future research opportunities
Data efficient deep learning for medical image analysis: A survey
The rapid evolution of deep learning has significantly advanced the field of
medical image analysis. However, despite these achievements, the further
enhancement of deep learning models for medical image analysis faces a
significant challenge due to the scarcity of large, well-annotated datasets. To
address this issue, recent years have witnessed a growing emphasis on the
development of data-efficient deep learning methods. This paper conducts a
thorough review of data-efficient deep learning methods for medical image
analysis. To this end, we categorize these methods based on the level of
supervision they rely on, encompassing categories such as no supervision,
inexact supervision, incomplete supervision, inaccurate supervision, and only
limited supervision. We further divide these categories into finer
subcategories. For example, we categorize inexact supervision into multiple
instance learning and learning with weak annotations. Similarly, we categorize
incomplete supervision into semi-supervised learning, active learning, and
domain-adaptive learning and so on. Furthermore, we systematically summarize
commonly used datasets for data efficient deep learning in medical image
analysis and investigate future research directions to conclude this survey.Comment: Under Revie
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