Learning Decision Ensemble using a Graph Neural Network for Comorbidity Aware Chest Radiograph Screening.

Chest radiographs are primarily employed for the screening of cardio, thoracic and pulmonary conditions. Machine learning based automated solutions are being developed to reduce the burden of routine screening on Radiologists, allowing them to focus on critical cases. While recent efforts demonstrate the use of ensemble of deep convolutional neural networks (CNN), they do not take disease comorbidity into consideration, thus lowering their screening performance.

A Systematic Search over Deep Convolutional Neural Network Architectures for Screening Chest Radiographs.

Chest radiographs are primarily employed for the screening of pulmonary and cardio-/thoracic conditions. Being undertaken at primary healthcare centers, they require the presence of an on-premise reporting Radiologist, which is a challenge in low and middle income countries. This has inspired the development of machine learning based automation of the screening process.

A Two-Stage Multiple Instance Learning Framework for the Detection of Breast Cancer in Mammograms.

Mammograms are commonly employed in the large scale screening of breast cancer which is primarily characterized by the presence of malignant masses. However, automated image-level detection of malignancy is a challenging task given the small size of the mass regions and difficulty in discriminating between malignant, benign mass and healthy dense fibro-glandular tissue. To address these issues, we explore a two-stage Multiple Instance Learning (MIL) framework.

Immunohistochemical evaluation of prime molecules in cervical lesions towards assessment of malignant potentiality.

Objective: A comparative immunohistochemical evaluation of p63, CD105, and E-cadherin expression pattern in histopathologically confirmed normal cervical epithelium (NCM), dysplastic cervical epithelium (DYS) and squamous cell carcinoma (SCC) of uterine cervix towards assessing malignant potentiality of the precancerous condition.

Materials And Methods: The biopsies from cervical mucosa (normal, dysplasia, and cancer) were studied by routine hematoxylin and eosin (H and E) and by immunohistochemistry for p63, E-cadherin, and CD105 expression. The expressions of these molecules were assessed in a semiquantitative way by (i) counting p63 cell population and distribution, (ii) intensity scoring of E-cadherin along the expression path, and (iii) measuring CD105 expression density.

Integrated cervical smear screening using liquid based cytology and bioimpedance analysis.

Objective: To minimize the false negativity in cervical cancer screening with Papanicolaou (Pap) test, there is a need to explore novel cytological technique and identification of unique and important cellular features from the perspectives of morphological as well as biophysical properties.

Materials And Methods: The present study explores the feasibility of low-cost cervical monolayer techniques in extracting cyto-pathological features to classify normal and abnormal conditions. The cervical cells were also analyzed in respect to their electrical bioimpedance.