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  1. Article ; Online: A Federated Learning Approach to Tumor Detection in Colon Histology Images.

    Gunesli, Gozde N / Bilal, Mohsin / Raza, Shan E Ahmed / Rajpoot, Nasir M

    Journal of medical systems

    2023  Volume 47, Issue 1, Page(s) 99

    Abstract: Federated learning (FL), a relatively new area of research in medical image analysis, enables collaborative learning of a federated deep learning model without sharing the data of participating clients. In this paper, we propose FedDropoutAvg, a new ... ...

    Abstract Federated learning (FL), a relatively new area of research in medical image analysis, enables collaborative learning of a federated deep learning model without sharing the data of participating clients. In this paper, we propose FedDropoutAvg, a new federated learning approach for detection of tumor in images of colon tissue slides. The proposed method leverages the power of dropout, a commonly employed scheme to avoid overfitting in neural networks, in both client selection and federated averaging processes. We examine FedDropoutAvg against other FL benchmark algorithms for two different image classification tasks using a publicly available multi-site histopathology image dataset. We train and test the proposed model on a large dataset consisting of 1.2 million image tiles from 21 different sites. For testing the generalization of all models, we select held-out test sets from sites that were not used during training. We show that the proposed approach outperforms other FL methods and reduces the performance gap (to less than 3% in terms of AUC on independent test sites) between FL and a central deep learning model that requires all data to be shared for centralized training, demonstrating the potential of the proposed FedDropoutAvg model to be more generalizable than other state-of-the-art federated models. To the best of our knowledge, ours is the first study to effectively utilize the dropout strategy in a federated setting for tumor detection in histology images.
    MeSH term(s) Humans ; Algorithms ; Benchmarking ; Colon/diagnostic imaging ; Knowledge ; Neural Networks, Computer
    Language English
    Publishing date 2023-09-16
    Publishing country United States
    Document type Journal Article
    ZDB-ID 423488-1
    ISSN 1573-689X ; 0148-5598
    ISSN (online) 1573-689X
    ISSN 0148-5598
    DOI 10.1007/s10916-023-01994-5
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  2. Article ; Online: Learning Where to See: A Novel Attention Model for Automated Immunohistochemical Scoring.

    Qaiser, Talha / Rajpoot, Nasir M

    IEEE transactions on medical imaging

    2019  Volume 38, Issue 11, Page(s) 2620–2631

    Abstract: Estimating over-amplification of human epidermal growth factor receptor 2 (HER2) on invasive breast cancer is regarded as a significant predictive and prognostic marker. We propose a novel deep reinforcement learning (DRL)-based model that treats ... ...

    Abstract Estimating over-amplification of human epidermal growth factor receptor 2 (HER2) on invasive breast cancer is regarded as a significant predictive and prognostic marker. We propose a novel deep reinforcement learning (DRL)-based model that treats immunohistochemical (IHC) scoring of HER2 as a sequential learning task. For a given image tile sampled from multi-resolution giga-pixel whole slide image (WSI), the model learns to sequentially identify some of the diagnostically relevant regions of interest (ROIs) by following a parameterized policy. The selected ROIs are processed by recurrent and residual convolution networks to learn the discriminative features for different HER2 scores and predict the next location, without requiring to process all the sub-image patches of a given tile for predicting the HER2 score, mimicking the histopathologist who would not usually analyze every part of the slide at the highest magnification. The proposed model incorporates a task-specific regularization term and inhibition of return mechanism to prevent the model from revisiting the previously attended locations. We evaluated our model on two IHC datasets: a publicly available dataset from the HER2 scoring challenge contest and another dataset consisting of WSIs of gastroenteropancreatic neuroendocrine tumor sections stained with Glo1 marker. We demonstrate that the proposed model outperforms other methods based on state-of-the-art deep convolutional networks. To the best of our knowledge, this is the first study using DRL for IHC scoring and could potentially lead to wider use of DRL in the domain of computational pathology reducing the computational burden of the analysis of large multi-gigapixel histology images.
    MeSH term(s) Algorithms ; Biomarkers, Tumor/analysis ; Breast/chemistry ; Breast/diagnostic imaging ; Breast Neoplasms/chemistry ; Breast Neoplasms/diagnostic imaging ; Breast Neoplasms/pathology ; Deep Learning ; Female ; Humans ; Image Interpretation, Computer-Assisted/methods ; Immunohistochemistry/methods ; Receptor, ErbB-2/analysis
    Chemical Substances Biomarkers, Tumor ; ERBB2 protein, human (EC 2.7.10.1) ; Receptor, ErbB-2 (EC 2.7.10.1)
    Language English
    Publishing date 2019-03-22
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 622531-7
    ISSN 1558-254X ; 0278-0062
    ISSN (online) 1558-254X
    ISSN 0278-0062
    DOI 10.1109/TMI.2019.2907049
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Consistency regularisation in varying contexts and feature perturbations for semi-supervised semantic segmentation of histology images.

    Bashir, Raja Muhammad Saad / Qaiser, Talha / Raza, Shan E Ahmed / Rajpoot, Nasir M

    Medical image analysis

    2023  Volume 91, Page(s) 102997

    Abstract: Semantic segmentation of various tissue and nuclei types in histology images is fundamental to many downstream tasks in the area of computational pathology (CPath). In recent years, Deep Learning (DL) methods have been shown to perform well on ... ...

    Abstract Semantic segmentation of various tissue and nuclei types in histology images is fundamental to many downstream tasks in the area of computational pathology (CPath). In recent years, Deep Learning (DL) methods have been shown to perform well on segmentation tasks but DL methods generally require a large amount of pixel-wise annotated data. Pixel-wise annotation sometimes requires expert's knowledge and time which is laborious and costly to obtain. In this paper, we present a consistency based semi-supervised learning (SSL) approach that can help mitigate this challenge by exploiting a large amount of unlabelled data for model training thus alleviating the need for a large annotated dataset. However, SSL models might also be susceptible to changing context and features perturbations exhibiting poor generalisation due to the limited training data. We propose an SSL method that learns robust features from both labelled and unlabelled images by enforcing consistency against varying contexts and feature perturbations. The proposed method incorporates context-aware consistency by contrasting pairs of overlapping images in a pixel-wise manner from changing contexts resulting in robust and context invariant features. We show that cross-consistency training makes the encoder features invariant to different perturbations and improves the prediction confidence. Finally, entropy minimisation is employed to further boost the confidence of the final prediction maps from unlabelled data. We conduct an extensive set of experiments on two publicly available large datasets (BCSS and MoNuSeg) and show superior performance compared to the state-of-the-art methods.
    MeSH term(s) Humans ; Semantics ; Cell Nucleus ; Entropy ; Histological Techniques ; Supervised Machine Learning ; Image Processing, Computer-Assisted
    Language English
    Publishing date 2023-10-11
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 1356436-5
    ISSN 1361-8423 ; 1361-8431 ; 1361-8415
    ISSN (online) 1361-8423 ; 1361-8431
    ISSN 1361-8415
    DOI 10.1016/j.media.2023.102997
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: A digital score of peri-epithelial lymphocytic activity predicts malignant transformation in oral epithelial dysplasia.

    Bashir, Raja Muhammad Saad / Shephard, Adam J / Mahmood, Hanya / Azarmehr, Neda / Raza, Shan E Ahmed / Khurram, Syed Ali / Rajpoot, Nasir M

    The Journal of pathology

    2023  Volume 260, Issue 4, Page(s) 431–442

    Abstract: Oral squamous cell carcinoma (OSCC) is amongst the most common cancers, with more than 377,000 new cases worldwide each year. OSCC prognosis remains poor, related to cancer presentation at a late stage, indicating the need for early detection to improve ... ...

    Abstract Oral squamous cell carcinoma (OSCC) is amongst the most common cancers, with more than 377,000 new cases worldwide each year. OSCC prognosis remains poor, related to cancer presentation at a late stage, indicating the need for early detection to improve patient prognosis. OSCC is often preceded by a premalignant state known as oral epithelial dysplasia (OED), which is diagnosed and graded using subjective histological criteria leading to variability and prognostic unreliability. In this work, we propose a deep learning approach for the development of prognostic models for malignant transformation and their association with clinical outcomes in histology whole slide images (WSIs) of OED tissue sections. We train a weakly supervised method on OED cases (n = 137) with malignant transformation (n = 50) and mean malignant transformation time of 6.51 years (±5.35 SD). Stratified five-fold cross-validation achieved an average area under the receiver-operator characteristic curve (AUROC) of 0.78 for predicting malignant transformation in OED. Hotspot analysis revealed various features of nuclei in the epithelium and peri-epithelial tissue to be significant prognostic factors for malignant transformation, including the count of peri-epithelial lymphocytes (PELs) (p < 0.05), epithelial layer nuclei count (NC) (p < 0.05), and basal layer NC (p < 0.05). Progression-free survival (PFS) using the epithelial layer NC (p < 0.05, C-index = 0.73), basal layer NC (p < 0.05, C-index = 0.70), and PELs count (p < 0.05, C-index = 0.73) all showed association of these features with a high risk of malignant transformation in our univariate analysis. Our work shows the application of deep learning for the prognostication and prediction of PFS of OED for the first time and offers potential to aid patient management. Further evaluation and testing on multi-centre data is required for validation and translation to clinical practice. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
    MeSH term(s) Humans ; Carcinoma, Squamous Cell/pathology ; Mouth Neoplasms/pathology ; Biomarkers, Tumor/analysis ; Hyperplasia/pathology ; Precancerous Conditions/pathology ; Squamous Cell Carcinoma of Head and Neck/pathology ; Lymphocytes/pathology ; Head and Neck Neoplasms/pathology
    Chemical Substances Biomarkers, Tumor
    Language English
    Publishing date 2023-06-09
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 3119-7
    ISSN 1096-9896 ; 0022-3417
    ISSN (online) 1096-9896
    ISSN 0022-3417
    DOI 10.1002/path.6094
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Ud II Zs.12: Show issues Location:
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    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

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  5. Book ; Online: Consistency Regularisation in Varying Contexts and Feature Perturbations for Semi-Supervised Semantic Segmentation of Histology Images

    Bashir, Raja Muhammad Saad / Qaiser, Talha / Raza, Shan E Ahmed / Rajpoot, Nasir M.

    2023  

    Abstract: Semantic segmentation of various tissue and nuclei types in histology images is fundamental to many downstream tasks in the area of computational pathology (CPath). In recent years, Deep Learning (DL) methods have been shown to perform well on ... ...

    Abstract Semantic segmentation of various tissue and nuclei types in histology images is fundamental to many downstream tasks in the area of computational pathology (CPath). In recent years, Deep Learning (DL) methods have been shown to perform well on segmentation tasks but DL methods generally require a large amount of pixel-wise annotated data. Pixel-wise annotation sometimes requires expert's knowledge and time which is laborious and costly to obtain. In this paper, we present a consistency based semi-supervised learning (SSL) approach that can help mitigate this challenge by exploiting a large amount of unlabelled data for model training thus alleviating the need for a large annotated dataset. However, SSL models might also be susceptible to changing context and features perturbations exhibiting poor generalisation due to the limited training data. We propose an SSL method that learns robust features from both labelled and unlabelled images by enforcing consistency against varying contexts and feature perturbations. The proposed method incorporates context-aware consistency by contrasting pairs of overlapping images in a pixel-wise manner from changing contexts resulting in robust and context invariant features. We show that cross-consistency training makes the encoder features invariant to different perturbations and improves the prediction confidence. Finally, entropy minimisation is employed to further boost the confidence of the final prediction maps from unlabelled data. We conduct an extensive set of experiments on two publicly available large datasets (BCSS and MoNuSeg) and show superior performance compared to the state-of-the-art methods.
    Keywords Computer Science - Computer Vision and Pattern Recognition
    Subject code 006
    Publishing date 2023-01-30
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Book ; Online: Mimicking a Pathologist

    Raza, Manahil / Awan, Ruqayya / Bashir, Raja Muhammad Saad / Qaiser, Talha / Rajpoot, Nasir M.

    Dual Attention Model for Scoring of Gigapixel Histology Images

    2023  

    Abstract: Some major challenges associated with the automated processing of whole slide images (WSIs) includes their sheer size, different magnification levels and high resolution. Utilizing these images directly in AI frameworks is computationally expensive due ... ...

    Abstract Some major challenges associated with the automated processing of whole slide images (WSIs) includes their sheer size, different magnification levels and high resolution. Utilizing these images directly in AI frameworks is computationally expensive due to memory constraints, while downsampling WSIs incurs information loss and splitting WSIs into tiles and patches results in loss of important contextual information. We propose a novel dual attention approach, consisting of two main components, to mimic visual examination by a pathologist. The first component is a soft attention model which takes as input a high-level view of the WSI to determine various regions of interest. We employ a custom sampling method to extract diverse and spatially distinct image tiles from selected high attention areas. The second component is a hard attention classification model, which further extracts a sequence of multi-resolution glimpses from each tile for classification. Since hard attention is non-differentiable, we train this component using reinforcement learning and predict the location of glimpses without processing all patches of a given tile, thereby aligning with pathologist's way of diagnosis. We train our components both separately and in an end-to-end fashion using a joint loss function to demonstrate the efficacy of our proposed model. We employ our proposed model on two different IHC use cases: HER2 prediction on breast cancer and prediction of Intact/Loss status of two MMR biomarkers, for colorectal cancer. We show that the proposed model achieves accuracy comparable to state-of-the-art methods while only processing a small fraction of the WSI at highest magnification.
    Keywords Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition
    Subject code 006
    Publishing date 2023-02-19
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article ; Online: Subcellular protein expression models for microsatellite instability in colorectal adenocarcinoma tissue images.

    Kovacheva, Violeta N / Rajpoot, Nasir M

    BMC bioinformatics

    2016  Volume 17, Issue 1, Page(s) 430

    Abstract: Background: New bioimaging techniques capable of visualising the co-location of numerous proteins within individual cells have been proposed to study tumour heterogeneity of neighbouring cells within the same tissue specimen. These techniques have ... ...

    Abstract Background: New bioimaging techniques capable of visualising the co-location of numerous proteins within individual cells have been proposed to study tumour heterogeneity of neighbouring cells within the same tissue specimen. These techniques have highlighted the need to better understand the interplay between proteins in terms of their colocalisation.
    Results: We recently proposed a cellular-level model of the healthy and cancerous colonic crypt microenvironments. Here, we extend the model to include detailed models of protein expression to generate synthetic multiplex fluorescence data. As a first step, we present models for various cell organelles learned from real immunofluorescence data from the Human Protein Atlas. Comparison between the distribution of various features obtained from the real and synthetic organelles has shown very good agreement. This has included both features that have been used as part of the model input and ones that have not been explicitly considered. We then develop models for six proteins which are important colorectal cancer biomarkers and are associated with microsatellite instability, namely MLH1, PMS2, MSH2, MSH6, P53 and PTEN. The protein models include their complex expression patterns and which cell phenotypes express them. The models have been validated by comparing distributions of real and synthesised parameters and by application of frameworks for analysing multiplex immunofluorescence image data.
    Conclusions: The six proteins have been chosen as a case study to illustrate how the model can be used to generate synthetic multiplex immunofluorescence data. Further proteins could be included within the model in a similar manner to enable the study of a larger set of proteins of interest and their interactions. To the best of our knowledge, this is the first model for expression of multiple proteins in anatomically intact tissue, rather than within cells in culture.
    MeSH term(s) Adenocarcinoma/genetics ; Adenocarcinoma/metabolism ; Biomarkers, Tumor/metabolism ; Colorectal Neoplasms/genetics ; Colorectal Neoplasms/metabolism ; DNA-Binding Proteins/metabolism ; Humans ; Immunohistochemistry ; Microsatellite Instability ; Microsatellite Repeats/genetics ; Protein Processing, Post-Translational ; Subcellular Fractions
    Chemical Substances Biomarkers, Tumor ; DNA-Binding Proteins
    Language English
    Publishing date 2016-10-22
    Publishing country England
    Document type Journal Article
    ZDB-ID 2041484-5
    ISSN 1471-2105 ; 1471-2105
    ISSN (online) 1471-2105
    ISSN 1471-2105
    DOI 10.1186/s12859-016-1243-y
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Book ; Online: All You Need is Color

    Dawood, Muhammad / Branson, Kim / Rajpoot, Nasir M. / Minhas, Fayyaz ul Amir Afsar

    Image based Spatial Gene Expression Prediction using Neural Stain Learning

    2021  

    Abstract: Is it possible to predict expression levels of different genes at a given spatial location in the routine histology image of a tumor section by modeling its stain absorption characteristics?" In this work, we propose a "stain-aware" machine learning ... ...

    Abstract "Is it possible to predict expression levels of different genes at a given spatial location in the routine histology image of a tumor section by modeling its stain absorption characteristics?" In this work, we propose a "stain-aware" machine learning approach for prediction of spatial transcriptomic gene expression profiles using digital pathology image of a routine Hematoxylin & Eosin (H&E) histology section. Unlike recent deep learning methods which are used for gene expression prediction, our proposed approach termed Neural Stain Learning (NSL) explicitly models the association of stain absorption characteristics of the tissue with gene expression patterns in spatial transcriptomics by learning a problem-specific stain deconvolution matrix in an end-to-end manner. The proposed method with only 11 trainable weight parameters outperforms both classical regression models with cellular composition and morphological features as well as deep learning methods. We have found that the gene expression predictions from the proposed approach show higher correlations with true expression values obtained through sequencing for a larger set of genes in comparison to other approaches.

    Comment: 14 pages, 4 figures, 1 table
    Keywords Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Artificial Intelligence ; Computer Science - Computer Vision and Pattern Recognition ; Quantitative Biology - Quantitative Methods
    Subject code 006 ; 004
    Publishing date 2021-08-23
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Book ; Online: ALBRT

    Dawood, Muhammad / Branson, Kim / Rajpoot, Nasir M. / Minhas, Fayyaz ul Amir Afsar

    Cellular Composition Prediction in Routine Histology Images

    2021  

    Abstract: Cellular composition prediction, i.e., predicting the presence and counts of different types of cells in the tumor microenvironment from a digitized image of a Hematoxylin and Eosin (H&E) stained tissue section can be used for various tasks in ... ...

    Abstract Cellular composition prediction, i.e., predicting the presence and counts of different types of cells in the tumor microenvironment from a digitized image of a Hematoxylin and Eosin (H&E) stained tissue section can be used for various tasks in computational pathology such as the analysis of cellular topology and interactions, subtype prediction, survival analysis, etc. In this work, we propose an image-based cellular composition predictor (ALBRT) which can accurately predict the presence and counts of different types of cells in a given image patch. ALBRT, by its contrastive-learning inspired design, learns a compact and rotation-invariant feature representation that is then used for cellular composition prediction of different cell types. It offers significant improvement over existing state-of-the-art approaches for cell classification and counting. The patch-level feature representation learned by ALBRT is transferrable for cellular composition analysis over novel datasets and can also be utilized for downstream prediction tasks in CPath as well. The code and the inference webserver for the proposed method are available at the URL: https://github.com/engrodawood/ALBRT.

    Comment: 11 pages, 5 figures
    Keywords Quantitative Biology - Quantitative Methods ; Electrical Engineering and Systems Science - Image and Video Processing
    Subject code 004
    Publishing date 2021-08-18
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article ; Online: Multi-resolution cell orientation congruence descriptors for epithelium segmentation in endometrial histology images.

    Li, Guannan / Raza, Shan E Ahmed / Rajpoot, Nasir M

    Medical image analysis

    2017  Volume 37, Page(s) 91–100

    Abstract: It has been recently shown that recurrent miscarriage can be caused by abnormally high ratio of number of uterine natural killer (UNK) cells to the number of stromal cells in human female uterus lining. Due to high workload, the counting of UNK and ... ...

    Abstract It has been recently shown that recurrent miscarriage can be caused by abnormally high ratio of number of uterine natural killer (UNK) cells to the number of stromal cells in human female uterus lining. Due to high workload, the counting of UNK and stromal cells needs to be automated using computer algorithms. However, stromal cells are very similar in appearance to epithelial cells which must be excluded in the counting process. To exclude the epithelial cells from the counting process it is necessary to identify epithelial regions. There are two types of epithelial layers that can be encountered in the endometrium: luminal epithelium and glandular epithelium. To the best of our knowledge, there is no existing method that addresses the segmentation of both types of epithelium simultaneously in endometrial histology images. In this paper, we propose a multi-resolution Cell Orientation Congruence (COCo) descriptor which exploits the fact that neighbouring epithelial cells exhibit similarity in terms of their orientations. Our experimental results show that the proposed descriptors yield accurate results in simultaneously segmenting both luminal and glandular epithelium.
    MeSH term(s) Algorithms ; Cell Count ; Endometrium/cytology ; Endometrium/diagnostic imaging ; Endometrium/immunology ; Epithelial Cells/cytology ; Epithelium/diagnostic imaging ; Female ; Humans ; Killer Cells, Natural/cytology ; Pregnancy ; Reproducibility of Results ; Stromal Cells/cytology
    Language English
    Publishing date 2017-01-22
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 1356436-5
    ISSN 1361-8423 ; 1361-8431 ; 1361-8415
    ISSN (online) 1361-8423 ; 1361-8431
    ISSN 1361-8415
    DOI 10.1016/j.media.2017.01.006
    Database MEDical Literature Analysis and Retrieval System OnLINE

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