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We briefly outline an algorithm for accurate quantification of specific binding of gold particles to fixed biological tissue samples prepared for immuno-transmission electron microscopy (TEM). The algorithm is based on existing protocols…
The Internet of Medical Things (IoMT) has dramatically benefited medical professionals that patients and physicians can access from all regions. Although the automatic detection and prediction of diseases such as melanoma and leukemia is…
Transfer learning (TL), the next frontier in machine learning (ML), has gained much popularity in recent years, due to the various challenges faced in ML, like the requirement of vast amounts of training data, expensive and time-consuming…
Machine-learning (ML) techniques are explored to identify and classify hadronic decays of highly Lorentz-boosted W/Z/Higgs bosons and top quarks. Techniques without ML have also been evaluated and are included for comparison. The…
The proliferation of sensors brings an immense volume of spatio-temporal (ST) data in many domains, including monitoring, diagnostics, and prognostics applications. Data curation is a time-consuming process for a large volume of data,…
Medical image classification plays an increasingly vital role in identifying various diseases by classifying medical images, such as X-rays, MRIs and CT scans, into different categories based on their features. In recent years, deep…
Leveraging scanning tunneling microscopy (STM) for atomic-scale fabrication has led to many advancements such as the creation of atomic electron-spin qubit structures on surfaces. However, the time-consuming and tedious nature of this…
Molecular representation is a critical element in our understanding of the physical world and the foundation for modern molecular machine learning. Previous molecular machine learning models have employed strings, fingerprints, global…
In this paper, we address the problem of Multiple Transmitter Localization (MTL). MTL is to determine the locations of potential multiple transmitters in a field, based on readings from a distributed set of sensors. In contrast to the…
This paper discusses the role of Transfer Learning (TL) and transformers in cancer detection based on image analysis. With the enormous evolution of cancer patients, the identification of cancer cells in a patient's body has emerged as a…
Lung cancer is one of the most deadly diseases in the world. Detecting such tumors at an early stage can be a tedious task. Existing deep learning architecture for lung nodule identification used complex architecture with large number of…
The process of identifying and characterizing B-cell epitopes, which are the portions of antigens recognized by antibodies, is important for our understanding of the immune system, and for many applications including vaccine development,…
Deep learning is currently the most important branch of machine learning, with applications in speech recognition, computer vision, image classification, and medical imaging analysis. Plant recognition is one of the areas where image…
This paper is devoted to the problem of identification of colloidal assemblies using the example of two-dimensional coatings (monolayer assemblies). Colloidal systems are used in various fields of science and technology, for example, in…
Increased levels of tumor infiltrating lymphocytes (TILs) in cancer tissue indicate favourable outcomes in many types of cancer. Manual quantification of immune cells is inaccurate and time consuming for pathologists. Our aim is to leverage…
Interference enhanced wide-field nanoparticle imaging is a highly sensitive technique that has found numerous applications in labeled and label-free sub-diffraction-limited pathogen detection. It also provides unique opportunities for…
Transfer learning (TL) from pretrained deep models is a standard practice in modern medical image classification (MIC). However, what levels of features to be reused are problem-dependent, and uniformly finetuning all layers of pretrained…
As an essential attribute of organic compounds, polarity has a profound influence on many molecular properties such as solubility and phase transition temperature. Thin layer chromatography (TLC) represents a commonly used technique for…
This work is motivated by multimodality breast cancer imaging data, which is quite challenging in that the signals of discrete tumor-associated microvesicles (TMVs) are randomly distributed with heterogeneous patterns. This imposes a…
Lung cancer is an extremely lethal disease primarily due to its late-stage diagnosis and significant mortality rate, making it the major cause of cancer-related demises globally. Machine Learning (ML) and Convolution Neural network (CNN)…