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Article
Open AccessA fully automated and explainable algorithm for predicting malignant transformation in oral epithelial dysplasia
Oral epithelial dysplasia (OED) is a premalignant histopathological diagnosis given to lesions of the oral cavity. Its grading suffers from significant inter-/intra-observer variability, and does not reliably ...
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Article
Open AccessAI-enabled routine H&E image based prognostic marker for early-stage luminal breast cancer
Breast cancer (BC) grade is a well-established subjective prognostic indicator of tumour aggressiveness. Tumour heterogeneity and subjective assessment result in high degree of variability among observers in B...
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Article
Open AccessEvaluation of tumour infiltrating lymphocytes in luminal breast cancer using artificial intelligence
Tumour infiltrating lymphocytes (TILs) are a prognostic parameter in triple-negative and human epidermal growth factor receptor 2 (HER2)-positive breast cancer (BC). However, their role in luminal (oestrogen r...
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Article
A Federated Learning Approach to Tumor Detection in Colon Histology Images
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 thi...
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Article
Open AccessTIAToolbox as an end-to-end library for advanced tissue image analytics
Computational pathology has seen rapid growth in recent years, driven by advanced deep-learning algorithms. Due to the sheer size and complexity of multi-gigapixel whole-slide images, to the best of our knowle...
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Chapter and Conference Paper
Morph-Net: End-to-End Prediction of Nuclear Morphological Features from Histology Images
Analysis using morphological features of different types of nuclei have been shown to be useful for many different tasks in computational pathology. To obtain morphological features of nuclei in an image, a ne...
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Chapter and Conference Paper
Cross-Stream Interactions: Segmentation of Lung Adenocarcinoma Growth Patterns
Lung adenocarcinoma has histologically distinct growth patterns that have been associated with patient prognosis. Precision segmentation of growth patterns in routine histology samples is challenging due to th...
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Chapter and Conference Paper
Nuclear Segmentation and Classification: On Color and Compression Generalization
Since the introduction of digital and computational pathology as a field, one of the major problems in the clinical application of algorithms has been the struggle to generalize well to examples outside the di...
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Chapter and Conference Paper
A Novel Framework for Coarse-Grained Semantic Segmentation of Whole-Slide Images
Semantic segmentation of multi-gigapixel whole-slide images (WSI) is fundamental to computational pathology, as segmentation of different tissue types and layers is a prerequisite for several downstream histol...
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Article
Open AccessBiomarkers for site-specific response to neoadjuvant chemotherapy in epithelial ovarian cancer: relating MRI changes to tumour cell load and necrosis
Diffusion-weighted magnetic resonance imaging (DW-MRI) potentially interrogates site-specific response to neoadjuvant chemotherapy (NAC) in epithelial ovarian cancer (EOC).
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Article
Open AccessUnmasking the immune microecology of ductal carcinoma in situ with deep learning
Despite increasing evidence supporting the clinical relevance of tumour infiltrating lymphocytes (TILs) in invasive breast cancer, TIL spatial variability within ductal carcinoma in situ (DCIS) samples and its...
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Article
Geospatial immune variability illuminates differential evolution of lung adenocarcinoma
Remarkable progress in molecular analyses has improved our understanding of the evolution of cancer cells toward immune escape1–5. However, the spatial configurations of immune and stromal cells, which may shed l...
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Chapter and Conference Paper
HydraMix-Net: A Deep Multi-task Semi-supervised Learning Approach for Cell Detection and Classification
Semi-supervised techniques have removed the barriers of large scale labelled set by exploiting unlabelled data to improve the performance of a model. In this paper, we propose a semi-supervised deep multi-task...
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Chapter and Conference Paper
MIMONet: Gland Segmentation Using Multi-Input-Multi-Output Convolutional Neural Network
Morphological assessment of glands in histopathology images is very important in cancer grading. However, this is labour intensive, requires highly trained pathologists and has limited reproducibility. Digitis...
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Article
Open AccessRobust normalization protocols for multiplexed fluorescence bioimage analysis
study of map** and interaction of co-localized proteins at a sub-cellular level is important for understanding complex biological phenomena. One of the recent techniques to map co-localized proteins is to us...
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Chapter and Conference Paper
A Novel Cell Orientation Congruence Descriptor for Superpixel Based Epithelium Segmentation in Endometrial Histology Images
Recurrent miscarriage can be caused by an abnormally high number of Uterine Natural Killer (UNK) cells in human female uterus lining. Recently a diagnosis protocol has been developed based on the ratio of UNK ...
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Chapter and Conference Paper
A Spatially Constrained Deep Learning Framework for Detection of Epithelial Tumor Nuclei in Cancer Histology Images
Detection of epithelial tumor nuclei in standard Hematoxylin & Eosin stained histology images is an essential step for the analysis of tissue architecture. The problem is quite challenging due to the high chro...
