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View article: Deep Learning Predicts <i>EGFR</i> Mutation Status from Histology Images in Non–Small Cell Lung Cancer
Deep Learning Predicts <i>EGFR</i> Mutation Status from Histology Images in Non–Small Cell Lung Cancer Open
EGFR mutation screening in non–small cell lung cancer (NSCLC) remains variable globally and represents a significant care gap despite international recommendations and molecular testing guidelines. Recently, the use of deep learning (DL) m…
View article: Stain-Invariant Representation for Tissue Classification in Histology Images
Stain-Invariant Representation for Tissue Classification in Histology Images Open
The process of digitising histology slides involves multiple factors that can affect a whole slide image's (WSI) final appearance, including the staining protocol, scanner, and tissue type. This variability constitutes a domain shift and r…
View article: Lung tumor segmentation in MRI mice scans using 3D nnU-Net with minimum annotations
Lung tumor segmentation in MRI mice scans using 3D nnU-Net with minimum annotations Open
In drug discovery, accurate lung tumor segmentation is an important step for assessing tumor size and its progression using \textit{in-vivo} imaging such as MRI. While deep learning models have been developed to automate this process, the …
View article: Can Medical Vision-Language Pre-training Succeed with Purely Synthetic Data?
Can Medical Vision-Language Pre-training Succeed with Purely Synthetic Data? Open
Medical Vision-Language Pre-training (MedVLP) has made significant progress in enabling zero-shot tasks for medical image understanding. However, training MedVLP models typically requires large-scale datasets with paired, high-quality imag…
View article: Utilizing Weak-to-Strong Consistency for Semi-Supervised Glomeruli Segmentation
Utilizing Weak-to-Strong Consistency for Semi-Supervised Glomeruli Segmentation Open
Accurate segmentation of glomerulus instances attains high clinical significance in the automated analysis of renal biopsies to aid in diagnosing and monitoring kidney disease. Analyzing real-world histopathology images often encompasses i…
View article: Consistency regularisation in varying contexts and feature perturbations for semi-supervised semantic segmentation of histology images
Consistency regularisation in varying contexts and feature perturbations for semi-supervised semantic segmentation of histology images Open
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 wel…
View article: Unsupervised Mutual Transformer Learning for Multi-Gigapixel Whole Slide Image Classification
Unsupervised Mutual Transformer Learning for Multi-Gigapixel Whole Slide Image Classification Open
Classification of gigapixel Whole Slide Images (WSIs) is an important prediction task in the emerging area of computational pathology. There has been a surge of research in deep learning models for WSI classification with clinical applicat…
View article: Dual Attention Model with Reinforcement Learning for Classification of Histology Whole-Slide Images
Dual Attention Model with Reinforcement Learning for Classification of Histology Whole-Slide Images Open
Digital whole slide images (WSIs) are generally captured at microscopic resolution and encompass extensive spatial data. Directly feeding these images to deep learning models is computationally intractable due to memory constraints, while …
View article: Consistency Regularisation in Varying Contexts and Feature Perturbations for Semi-Supervised Semantic Segmentation of Histology Images
Consistency Regularisation in Varying Contexts and Feature Perturbations for Semi-Supervised Semantic Segmentation of Histology Images Open
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 wel…
View article: Knowledge Distillation in Histology Landscape by Multi-Layer Features Supervision
Knowledge Distillation in Histology Landscape by Multi-Layer Features Supervision Open
Automatic tissue classification is a fundamental task in computational pathology for profiling tumor micro-environments. Deep learning has advanced tissue classification performance at the cost of significant computational power. Shallow n…
View article: Sensor Data Fusion Based on Deep Learning for Computer Vision Applications and Medical Applications
Sensor Data Fusion Based on Deep Learning for Computer Vision Applications and Medical Applications Open
Sensor fusion is the process of merging data from many sources, such as radar, lidar and camera sensors, to provide less uncertain information compared to the information collected from single source [...]
View article: Development of machine learning support for reading whole body diffusion-weighted MRI (WB-MRI) in myeloma for the detection and quantification of the extent of disease before and after treatment (MALIMAR): protocol for a cross-sectional diagnostic test accuracy study
Development of machine learning support for reading whole body diffusion-weighted MRI (WB-MRI) in myeloma for the detection and quantification of the extent of disease before and after treatment (MALIMAR): protocol for a cross-sectional diagnostic test accuracy study Open
Introduction Whole-body MRI (WB-MRI) is recommended by the National Institute of Clinical Excellence as the first-line imaging tool for diagnosis of multiple myeloma. Reporting WB-MRI scans requires expertise to interpret and can be challe…
View article: Multiple Instance Learning with Auxiliary Task Weighting for Multiple Myeloma Classification
Multiple Instance Learning with Auxiliary Task Weighting for Multiple Myeloma Classification Open
Whole body magnetic resonance imaging (WB-MRI) is the recommended modality for diagnosis of multiple myeloma (MM). WB-MRI is used to detect sites of disease across the entire skeletal system, but it requires significant expertise and is ti…
View article: Classification of COVID-19 via Homology of CT-SCAN
Classification of COVID-19 via Homology of CT-SCAN Open
In this worldwide spread of SARS-CoV-2 (COVID-19) infection, it is of utmost importance to detect the disease at an early stage especially in the hot spots of this epidemic. There are more than 110 Million infected cases on the globe, sofa…
View article: Methods for Segmentation and Classification of Digital Microscopy Tissue Images
Methods for Segmentation and Classification of Digital Microscopy Tissue Images Open
High-resolution microscopy images of tissue specimens provide detailed information about the morphology of normal and diseased tissue. Image analysis of tissue morphology can help cancer researchers develop a better understanding of cancer…
View article: Leveraging Unlabeled Whole-Slide-Images for Mitosis Detection
Leveraging Unlabeled Whole-Slide-Images for Mitosis Detection Open
Mitosis count is an important biomarker for prognosis of various cancers. At present, pathologists typically perform manual counting on a few selected regions of interest in breast whole-slide-images (WSIs) of patient biopsies. This task i…
View article: Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer
Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer Open
In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm …
View article: Glyoxalase 1 copy number variation in patients with well differentiated gastro-entero-pancreatic neuroendocrine tumours (GEP-NET)
Glyoxalase 1 copy number variation in patients with well differentiated gastro-entero-pancreatic neuroendocrine tumours (GEP-NET) Open
GLO1 copy-number was increased in a large percentage of patients with GEP-NET and correlated positively with increased Glo1 protein in tumour tissue. Analysis of GLO1 copy-number variation particularly in patients with midgut NET could be …
View article: <scp>HER</scp>2 challenge contest: a detailed assessment of automated <scp>HER</scp>2 scoring algorithms in whole slide images of breast cancer tissues
<span>HER</span>2 challenge contest: a detailed assessment of automated <span>HER</span>2 scoring algorithms in whole slide images of breast cancer tissues Open
Aims Evaluating expression of the human epidermal growth factor receptor 2 ( HER 2) by visual examination of immunohistochemistry ( IHC ) on invasive breast cancer ( BC a) is a key part of the diagnostic assessment of BC a due to its recog…
View article: An Integrated Environment For Tissue Morphometrics And Analytics
An Integrated Environment For Tissue Morphometrics And Analytics Open
Introduction/ Background Attaining high reproducibility in cancer diagnosis is still one of the main challenges in modern pathology due to subjectivity [1] [2] [3]. An integrated framework to extract quantitative morphological features fro…
View article: Persistent Homology for Fast Tumor Segmentation in Whole Slide Histology Images
Persistent Homology for Fast Tumor Segmentation in Whole Slide Histology Images Open
Automated tumor segmentation in Hematoxylin & Eosin stained histology images is an essential step towards a computer-aided diagnosis system. In this work we propose a novel tumor segmentation approach for a histology whole-slide image (WSI…