AIMC Topic: Histological Techniques
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Deep active learning for suggestive segmentation of biomedical image stacks via optimisation of Dice scores and traced boundary length.
Medical image analysis
Jul 24, 2022
Manual segmentation of stacks of 2D biomedical images (e.g., histology) is a time-consuming task which can be sped up with semi-automated techniques. In this article, we present a suggestive deep active learning framework that seeks to minimise the a...
CS-CO: A Hybrid Self-Supervised Visual Representation Learning Method for H&E-stained Histopathological Images.
Medical image analysis
Jul 20, 2022
Visual representation extraction is a fundamental problem in the field of computational histopathology. Considering the powerful representation capacity of deep learning and the scarcity of annotations, self-supervised learning has emerged as a promi...
SONNET: A Self-Guided Ordinal Regression Neural Network for Segmentation and Classification of Nuclei in Large-Scale Multi-Tissue Histology Images.
IEEE journal of biomedical and health informatics
Jul 1, 2022
Automated nuclei segmentation and classification are the keys to analyze and understand the cellular characteristics and functionality, supporting computer-aided digital pathology in disease diagnosis. However, the task still remains challenging due ...
Nucleus classification in histology images using message passing network.
Medical image analysis
May 14, 2022
Identification of nuclear components in the histology landscape is an important step towards developing computational pathology tools for the profiling of tumor micro-environment. Most existing methods for the identification of such components are li...
Non-invasive scoring of cellular atypia in keratinocyte cancers in 3D LC-OCT images using Deep Learning.
Scientific reports
Jan 10, 2022
Diagnosis based on histopathology for skin cancer detection is today's gold standard and relies on the presence or absence of biomarkers and cellular atypia. However it suffers drawbacks: it requires a strong expertise and is time-consuming. Moreover...
SAFRON: Stitching Across the Frontier Network for Generating Colorectal Cancer Histology Images.
Medical image analysis
Dec 29, 2021
Automated synthesis of histology images has several potential applications including the development of data-efficient deep learning algorithms. In the field of computational pathology, where histology images are large in size and visual context is c...
Computed tomography radiomics for the prediction of thymic epithelial tumor histology, TNM stage and myasthenia gravis.
PloS one
Dec 20, 2021
OBJECTIVES: To evaluate CT-derived radiomics for machine learning-based classification of thymic epithelial tumor (TET) stage (TNM classification), histology (WHO classification) and the presence of myasthenia gravis (MG).
High-Throughput, Label-Free and Slide-Free Histological Imaging by Computational Microscopy and Unsupervised Learning.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Nov 7, 2021
Rapid and high-resolution histological imaging with minimal tissue preparation has long been a challenging and yet captivating medical pursuit. Here, the authors propose a promising and transformative histological imaging method, termed computational...
Hierarchical graph representations in digital pathology.
Medical image analysis
Oct 27, 2021
Cancer diagnosis, prognosis, and therapy response predictions from tissue specimens highly depend on the phenotype and topological distribution of constituting histological entities. Thus, adequate tissue representations for encoding histological ent...
Dynamic Learning Rate in Deep CNN Model for Metastasis Detection and Classification of Histopathology Images.
Computational and mathematical methods in medicine
Oct 26, 2021
Diagnosis of different breast cancer stages using histopathology whole slide images (WSI) is the gold standard in determining the grade of tissue metastasis. Computer-aided diagnosis (CAD) assists medical experts as a second opinion tool in early det...
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