LAMA: Lesion-Aware Mixup Augmentation for Skin Lesion Segmentation.

Deep learning can exceed dermatologists' diagnostic accuracy in experimental image environments. However, inaccurate segmentation of images with multiple skin lesions can be seen with current methods. Thus, information present in multiple-lesion images, available to specialists, is not retrievable by machine learning.

Hybrid Topological Data Analysis and Deep Learning for Basal Cell Carcinoma Diagnosis.

A critical clinical indicator for basal cell carcinoma (BCC) is the presence of telangiectasia (narrow, arborizing blood vessels) within the skin lesions. Many skin cancer imaging processes today exploit deep learning (DL) models for diagnosis, segmentation of features, and feature analysis. To extend automated diagnosis, recent computational intelligence research has also explored the field of Topological Data Analysis (TDA), a branch of mathematics that uses topology to extract meaningful information from highly complex data.

Basal Cell Carcinoma Diagnosis with Fusion of Deep Learning and Telangiectasia Features.

In recent years, deep learning (DL) has been used extensively and successfully to diagnose different cancers in dermoscopic images. However, most approaches lack clinical inputs supported by dermatologists that could aid in higher accuracy and explainability. To dermatologists, the presence of telangiectasia, or narrow blood vessels that typically appear serpiginous or arborizing, is a critical indicator of basal cell carcinoma (BCC).

SharpRazor: Automatic removal of hair and ruler marks from dermoscopy images.

Background: The removal of hair and ruler marks is critical in handcrafted image analysis of dermoscopic skin lesions. No other dermoscopic artifacts cause more problems in segmentation and structure detection.

Purpose: The aim of the work is to detect both white and black hair, artifacts and finally inpaint correctly the image.

Skin Lesion Segmentation in Dermoscopic Images with Noisy Data.

We propose a deep learning approach to segment the skin lesion in dermoscopic images. The proposed network architecture uses a pretrained EfficientNet model in the encoder and squeeze-and-excitation residual structures in the decoder. We applied this approach on the publicly available International Skin Imaging Collaboration (ISIC) 2017 Challenge skin lesion segmentation dataset.

Part II: Missouri's Fentanyl Poisonings Rise to Record Levels.

Missouri's dramatic rise in fentanyl-related overdoses was reported in Part I of this two-part series. In Part II, we report that previous efforts to combat the surge in illicit fentanyl supply from China failed, as Chinese factories shifted production to basic fentanyl precursor chemicals, known as dual-use pre-precursors. Mexican drug cartels now synthesize fentanyl from these basic chemicals and have overpowered the Mexican government.

Improving Automatic Melanoma Diagnosis Using Deep Learning-Based Segmentation of Irregular Networks.

Deep learning has achieved significant success in malignant melanoma diagnosis. These diagnostic models are undergoing a transition into clinical use. However, with melanoma diagnostic accuracy in the range of ninety percent, a significant minority of melanomas are missed by deep learning.

Part I: Missouri's Fentanyl Poisonings Rise to Record Levels.

Missourians are dying of fentanyl poisoning at an unprecedented rate. We identified growth areas in Missouri for fatal fentanyl encounters in rural and western counties. Though the deaths occur for a multitude of reasons, a growing trend adds to the surge in fentanyl fatalities: poisonings from counterfeit pills.

ChimeraNet: U-Net for Hair Detection in Dermoscopic Skin Lesion Images.

Hair and ruler mark structures in dermoscopic images are an obstacle preventing accurate image segmentation and detection of critical network features. Recognition and removal of hairs from images can be challenging, especially for hairs that are thin, overlapping, faded, or of similar color as skin or overlaid on a textured lesion. This paper proposes a novel deep learning (DL) technique to detect hair and ruler marks in skin lesion images.

Optical Oxygen Sensor Patch Printed with Polystyrene Microparticles-based Ink on Flexible Substrate.

Optical oxygen sensors based on photoluminescence quenching have gained increasing attention as a superior method for continuous monitoring of oxygen in a growing number of applications. A simple and low-cost fabrication technique was developed to produce sensor arrays capable of two-dimensional oxygen tension measurement. Sensor patches were printed on polyvinylidene chloride film using an oxygen-sensitive ink cocktail, prepared by immobilizing Pt(II) mesotetra(pentafluorophenyl)porphine (PtTFPP) in monodispersed polystyrene microparticles.

Identification and quantification of 11 airborne biochemicals emitted by the brown recluse and another primitive hunting spider using headspace solid-phase microextraction-GC/MS.

Loxosceles reclusa, or brown recluse spider, is a harmful household spider whose habitat extends throughout the Midwest in the USA and other regions in the world. The pheromones and other biomolecules that facilitate signaling for brown recluses and other spider species are poorly understood. A rapid and sensitive method is needed to analyze airborne spider signaling biomolecules to better understand the structure and function of these biochemicals in order to control the population of the spiders.

Automated Cervical Digitized Histology Whole-Slide Image Analysis Toolbox.

Background: Cervical intraepithelial neoplasia (CIN) is regarded as a potential precancerous state of the uterine cervix. Timely and appropriate early treatment of CIN can help reduce cervical cancer mortality. Accurate estimation of CIN grade correlated with human papillomavirus type, which is the primary cause of the disease, helps determine the patient's risk for developing the disease.

DeepCIN: Attention-Based Cervical histology Image Classification with Sequential Feature Modeling for Pathologist-Level Accuracy.

Background: Cervical cancer is one of the deadliest cancers affecting women globally. Cervical intraepithelial neoplasia (CIN) assessment using histopathological examination of cervical biopsy slides is subject to interobserver variability. Automated processing of digitized histopathology slides has the potential for more accurate classification for CIN grades from normal to increasing grades of pre-malignancy: CIN1, CIN2, and CIN3.

Missouri's Fentanyl Problem: The China Connection.

Recently, Missouri has followed an overall upward trend in opioid overdose deaths. In 2018, Missouri was the state with the largest absolute and percentage increase in opioid-related overdose fatality rates per capita over the previous year (18.3% and 3.

EpithNet: Deep Regression for Epithelium Segmentation in Cervical Histology Images.

Background: Automated pathology techniques for detecting cervical cancer at the premalignant stage have advantages for women in areas with limited medical resources.

Methods: This article presents EpithNet, a deep learning approach for the critical step of automated epithelium segmentation in digitized cervical histology images. EpithNet employs three regression networks of varying dimensions of image input blocks (patches) surrounding a given pixel, with all blocks at a fixed resolution, using varying network depth.

The 5 P's of Pyoderma Gangrenosum.

The diagnosis of pyoderma gangrenosum (PG) is often difficult to establish based on a clinical presentation, which can mimic other dermatologic conditions. The formation of a mnemonic that incorporates the most prevalent clinical features of PG could aid in accuracy and speed of diagnosis. The 5 P's of PG: Painful, Progressive, Purple, Pretibial, Pathergy, and systemic associations, incorporate parameters recognizable on the first encounter with a patient with PG without reliance on histopathology and laboratory findings or treatment response.

Microparticles for Suspension Culture of Mammalian Cells.

The focus of this work is to develop a technology for the synthesis of polymer microcarriers that demonstrate mammalian cell culture adhesion on the surface of the microcarriers. Most mammalian cells are adherent in nature that requires multilayer vessels, large volume, expensive cell culture media, high manufacturing time, and high costs of cell culture supplies for the commercial-scale manufacturing of cells. The development of an efficient, scalable technology for producing large volumes of cells is a need in bioprocess industries to improve product potency.

Automatic lesion border selection in dermoscopy images using morphology and color features.

Purpose: We present a classifier for automatically selecting a lesion border for dermoscopy skin lesion images, to aid in computer-aided diagnosis of melanoma. Variation in photographic technique of dermoscopy images makes segmentation of skin lesions a difficult problem. No single algorithm provides an acceptable lesion border to allow further processing of skin lesions.

CELLULITIS: A mnemonic to increase accuracy of cellulitis diagnosis.

Cellulitis, a bacterial infection of the skin and subcutaneous tissue, is often misdiagnosed. Cellulitis accounts for a large number of all infectious disease-related hospitalizations in the U.S.