Latest AI and machine learning research in pathology for healthcare professionals.
Immunofluorescence microscopy is routinely used to visualise the spatial distribution of proteins that dictates their cellular function. However, unspecific antibody binding often results in high cytosolic background signals, decreasing the image contrast of a target structure. Recently, convolutional neural networks (CNNs) were successfully employed for image restoration in immunofluorescence mic...
Most plant diseases have apparent signs, and today's recognized method is for an expert plant pathologist to identify the disease by looking at infected plant leaves using a microscope. The fact is that manually diagnosing diseases is time consuming and that the effectiveness of the diagnosis is related to the pathologist's talents, making this a great application area for computer-aided diagnosti...
INTRODUCTION: Urine cytology offers a rapid and relatively inexpensive method to diagnose urothelial neoplasia. In our setting of a public sector labo...
Increasingly, highly multiplexed tissue imaging methods are used to profile protein expression at the single-cell level. However, a critical limitatio...
Histopathology reports are a primary data source for the case definition phase of a Cancer Registry. By reading the histopathology report, the operato...
The growing influence of artificial intelligence on radiology not only leads to a fundamental change in the way diagnoses are made but also creates a ...
Image fusion can be performed on images either in spatial domain or frequency domain methods. Frequency domain methods will be most preferred because ...
Fourier ptychographic microscopy (FPM) is a potential imaging technique, which is used to achieve wide field-of-view (FOV), high-resolution and quanti...
Biopsies of inflammatory tissue contain a complex network of interacting cells, orchestrating the immune or autoimmune response. While standard histol...
Melanoma detection, prognosis, and treatment represent challenging and complex areas of cutaneous oncology with considerable impact on patient outcome...
Tissue-engineered living machines is an emerging discipline that employs complex interactions between living cells and engineered scaffolds to self-as...
Quantitative ultrasound (QUS), which attempts to extract quantitative features from the US radiofrequency (RF) or envelope data for tissue characteriz...
The use of artificial intelligence methods in the image-based diagnostic assessment of hematological diseases is a growing trend in recent years. In t...
Identifying the lung carcinoma subtype in small biopsy specimens is an important part of determining a suitable treatment plan but is often challengin...
Genetic algorithms have a proven capability to explore a large space of solutions, and deal with very large numbers of input features. We hypothesized...
BACKGROUND: In malignant pleural mesothelioma (MPM), complex tumour morphology results in inconsistent radiological response assessment. Promising vol...
Deep neural networks are increasingly being used for computer-aided diagnosis, but erroneous diagnoses can be extremely costly for patients. We propos...
Temperature monitoring in ultrasound (US) imaging is important for various medical treatments, such as high-intensity focused US (HIFU) therapy or hyp...
Herbarium sheets present a unique view of the world's botanical history, evolution, and biodiversity. This makes them an all-important data source for...
In order to correctly obtain normal tissues and organs and tumor lesions, the research on multimodal medical image segmentation based on deep learning...