Utilizing Pseudo Color Image to Improve the Performance of Deep Transfer Learning-Based Computer-Aided Diagnosis Schemes in Breast Mass Classification.

The purpose of this study is to investigate the impact of using morphological information in classifying suspicious breast lesions. The widespread use of deep transfer learning can significantly improve the performance of the mammogram based CADx schemes. However, digital mammograms are grayscale images, while deep learning models are typically optimized using the natural images containing three channels.

A multi-stage fusion framework to classify breast lesions using deep learning and radiomics features computed from four-view mammograms.

Background: Developing computer aided diagnosis (CAD) schemes of mammograms to classify between malignant and benign breast lesions has attracted a lot of research attention over the last several decades. However, unlike radiologists who make diagnostic decisions based on the fusion of image features extracted from multi-view mammograms, most CAD schemes are single-view-based schemes, which limit CAD performance and clinical utility.

Purpose: This study aims to develop and test a novel CAD framework that optimally fuses information extracted from ipsilateral views of bilateral mammograms using both deep transfer learning (DTL) and radiomics feature extraction methods.

Quantifying Intracellular Nanoparticle Distributions with Three-Dimensional Super-Resolution Microscopy.

Super-resolution microscopy can transform our understanding of nanoparticle-cell interactions. Here, we established a super-resolution imaging technology to visualize nanoparticle distributions inside mammalian cells. The cells were exposed to metallic nanoparticles and then embedded within different swellable hydrogels to enable quantitative three-dimensional (3D) imaging approaching electron-microscopy-like resolution using a standard light microscope.

Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction.

Breast cancer remains the most diagnosed cancer in women. Advances in medical imaging modalities and technologies have greatly aided in the early detection of breast cancer and the decline of patient mortality rates. However, reading and interpreting breast images remains difficult due to the high heterogeneity of breast tumors and fibro-glandular tissue, which results in lower cancer detection sensitivity and specificity and large inter-reader variability.

Improving mammography lesion classification by optimal fusion of handcrafted and deep transfer learning features.

Handcrafted radiomics features or deep learning model-generated automated features are commonly used to develop computer-aided diagnosis schemes of medical images. The objective of this study is to test the hypothesis that handcrafted and automated features contain complementary classification information and fusion of these two types of features can improve CAD performance.We retrospectively assembled a dataset involving 1535 lesions (740 malignant and 795 benign).

Improved pentamethine cyanine nanosensors for optoacoustic imaging of pancreatic cancer.

Optoacoustic imaging is a new biomedical imaging technology with clear benefits over traditional optical imaging and ultrasound. While the imaging technology has improved since its initial development, the creation of dedicated contrast agents for optoacoustic imaging has been stagnant. Current exploration of contrast agents has been limited to standard commercial dyes that have already been established in optical imaging applications.

Molecular Imaging of Inflammatory Disease.

Inflammatory diseases include a wide variety of highly prevalent conditions with high mortality rates in severe cases ranging from cardiovascular disease, to rheumatoid arthritis, to chronic obstructive pulmonary disease, to graft vs. host disease, to a number of gastrointestinal disorders. Many diseases that are not considered inflammatory per se are associated with varying levels of inflammation.

Development of Multispectral Optoacoustic Tomography as a Clinically Translatable Modality for Cancer Imaging.

The use of optoacoustic imaging takes advantage of the photoacoustic effect to generate high-contrast, high-resolution medical images at penetration depths of up to 5 cm. Multispectral optoacoustic tomography (MSOT) is a type of optoacoustic imaging system that has seen promising preclinical success with a recent emergence into the clinic. Multiwavelength illumination of tissue allows for the mapping of multiple chromophores, which are generated endogenously or exogenously.

The neutral red assay can be used to evaluate cell viability during autophagy or in an acidic microenvironment in vitro.

Harsh conditions within the tumor microenvironment, such as hypoxia and extracellular acidic pH (pH), inactivate some chemotherapies, which results in limited or no cytotoxicity. Standard MTT, ATPlite and protease assays that are used to determine the potency of newly developed drugs often give erroneous results when applied under hypoxic or acidic conditions. Therefore, development of a cytotoxicity assay that does not yield false positive or false negative results under circumstances of both hypoxia and acidic pH is needed.

Imaging Inflammation and Infection in the Gastrointestinal Tract.

A variety of seemingly non-specific symptoms manifest within the gastrointestinal (GI) tract, particularly in the colon, in response to inflammation, infection, or a combination thereof. Differentiation between symptom sources can often be achieved using various radiologic studies. Although it is not possible to provide a comprehensive survey of imaging gastrointestinal GI tract infections in a single article, the purpose of this review is to survey several topics on imaging of GI tract inflammation and infections.

GABAergic signaling in young granule cells in the adult rat and mouse dentate gyrus.

Throughout most of the developing brain, including the hippocampus, GABAergic synapses are the first to become functional. Several features of GABAergic signaling change across development, suggesting that this signaling in the immature brain may play important roles in the growth of young neurons and the establishment of networks. To determine whether GABA(A) receptor (GABA(A)R)-containing synapses in new neurons born in the adult dentate gyrus have similar immature features, we examined spontaneous and evoked GABA(A)R-mediated synaptic currents in young (POMC-EGFP or doublecortin-immunostained) granule cells in acute slice preparations from adult mice and rats.