The automated detection of proliferative diabetic retinopathy using dual ensemble classification

Objective: Diabetic retinopathy (DR) is a retinal vascular disease that is caused by complications of diabetes. Proliferative diabetic retinopathy (PDR) is the advanced stage of the disease which carries a high risk of severe visual impairment. This stage is characterized by the growth of abnormal new vessels. We aim to develop a method for the automated detection of new vessels from retinal images. Methods: This method is based on a dual classification approach. Two vessel segmentation approaches are applied to create two separate binary vessel maps which each hold vital information. Local morphology, gradient and intensity features are measured using each binary vessel map to produce two separate 21-D feature vectors. Independent classification is performed for each feature vector using an ensemble system of bagged decision trees. These two independent outcomes are then combined to a produce a final decision. Results: Sensitivity and specificity results using a dataset of 60 images are 1.0000 and 0.9500 on a per image basis. Conclusions: The described automated system is capable of detecting the presence of new vessels

A Descriptive Study on Patterns of Traumatic Spinal Injuries in a Tertiary Care Hospital Rawalpindi

Introduction: Spinal injuries are one of the most debilitating injuries if not fatal and affect every dimension of patients' lives i.e. early mortality and late complications. Lifelong disability due to spinal cord injury is very common even if the patient survives early death. The current study was aimed to investigate the frequency, management, mortality, the pattern of spinal injuries and to recommend plans for better patient management based on assessment.Material and Methods: The study was descriptive, cross-sectional, and was conducted at the Neurosurgery Department of Rawalpindi Medical University and Allied Hospitals for the duration of October 2018 to January 2019. All cases of traumatic spinal injuries were included and variables noted were gender, age, mode of Injury, presenting motor power in limbs, ASIA score, diagnosis, management, outcome, and deaths.Results: In the sample size of 84 patients, the mean age was 37.1 years, the mechanism of injury due to falls was most common at 73%, the lumbar region was found to be the most common area involved. Male patients outnumbered females in the study. 14% of the patients could not survive due to the injury, 15% received cervical traction, 4% received cervical traction and anterior cervical plating, and 43% of patients underwent Transpedicular Screw Fixation, the total number of patients who expired was 25%.Conclusion: Patterns of traumatic spinal injuries are changing, shifting from road traffic accidents to falls being the primary cause nowadays, with prolonged hospital stay periods, disability for life, and high-cost treatments putting a huge burden on our already exhausted health resources. Efforts should be made to make a national registry for traumatic spinal injuries presented to the emergency department and guidelines should be established regarding occupational hazards. Awareness should be given to the general population regarding hazards at home regarding falls

Automated grade classification of oral epithelial dysplasia using morphometric analysis of histology images

Oral dysplasia is a pre-malignant stage of oral epithelial carcinomas, e.g., oral squamous cell carcinoma, where significant changes in tissue layers and cells can be observed under the microscope. However, malignancy can be reverted or cured using proper medication or surgery if the grade of malignancy is assessed properly. The assessment of correct grade is therefore critical in patient management as it can change the treatment decisions and prognosis for the dysplastic lesion. This assessment is highly challenging due to considerable inter- and intraobserver variability in pathologists’ agreement, which highlights the need for an automated grading system that can predict more accurate and reliable grade. Recent advancements have made it possible for digital pathology (DP) and artificial intelligence (AI) to join forces from the digitization of tissue slides into images and using those images to train and predict more accurate grades using complex AI models. In this regard, we propose a novel morphometric approach exploiting the architectural features in dysplastic lesions i.e., irregular epithelial stratification where we measure the widths of different layers of the epithelium from the boundary layer i.e., keratin projecting inwards to the epithelium and basal layers to the rest of the tissue section from a clinically significant viewpoint

Dense-CaptionNet : a sentence generation architecture for fine-grained description of image semantics

Automatic image captioning, a highly challenging research problem, aims to understand and describe the contents of the complex scene in human understandable natural language. The majority of the recent solutions are based on holistic approaches where the scene is described as a whole, potentially losing the important semantic relationship of objects in the scene. We propose Dense-CaptionNet, a region-based deep architecture for fine-grained description of image semantics, which localizes and describes each object/region in the image separately and generates a more detailed description of the scene. The proposed network contains three components which work together to generate a fine-grained description of image semantics. Region descriptions and object relationships are generated by the first module, whereas the second one generates the attributes of objects present in the scene. The textual descriptions obtained as an output of the two modules are concatenated to feed as an input to the sentence generation module, which works on encoder-decoder formulation to generate a grammatically correct but single line, fine-grained description of the whole scene. The proposed Dense-CaptionNet is trained and tested using Visual Genome, MSCOCO, and IAPR TC-12 datasets. The results establish a new state-of-the-art when compared with the existing top performing methodologies, e.g., Up-Down-Captioner, Show, Attend and Tell, Semstyle, and Neural Talk, especially on complex scenes

Context-aware convolutional neural network for grading of colorectal cancer histology images

Digital histology images are amenable to the application of convolutional neural networks (CNNs) for analysis due to the sheer size of pixel data present in them. CNNs are generally used for representation learning from small image patches (e.g. 224 × 224) extracted from digital histology images due to computational and memory constraints. However, this approach does not incorporate high-resolution contextual information in histology images. We propose a novel way to incorporate a larger context by a context-aware neural network based on images with a dimension of 1792 × 1792 pixels. The proposed framework first encodes the local representation of a histology image into high dimensional features then aggregates the features by considering their spatial organization to make a final prediction. We evaluated the proposed method on two colorectal cancer datasets for the task of cancer grading. Our method outperformed the traditional patch-based approaches, problem-specific methods, and existing context-based methods. We also presented a comprehensive analysis of different variants of the proposed method

Contribution of BRCA1 germline mutation in patients with sporadic breast cancer

Hereditary artifacts in BRCA1 gene have a significant contributory role in familial cases of breast cancer. However, its germline mutational penetrance in sporadic breast cancer cases with respect to Pakistani population has not yet been very well defined. This study was designed to assess the contributory role of germline mutations of this gene in sporadic cases of breast cancer. 150 cases of unilateral breast cancer patients, with no prior family history of breast cancer and no other disorders or diseases in general with age range 35–75 yrs, were included in this study

A novel digital score for abundance of tumour infiltrating lymphocytes predicts disease free survival in oral squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) is the most common type of head and neck (H&N) cancers with an increasing worldwide incidence and a worsening prognosis. The abundance of tumour infiltrating lymphocytes (TILs) has been shown to be a key prognostic indicator in a range of cancers with emerging evidence of its role in OSCC progression and treatment response. However, the current methods of TIL analysis are subjective and open to variability in interpretation. An automated method for quantification of TIL abundance has the potential to facilitate better stratification and prognostication of oral cancer patients. We propose a novel method for objective quantification of TIL abundance in OSCC histology images. The proposed TIL abundance (TILAb) score is calculated by first segmenting the whole slide images (WSIs) into underlying tissue types (tumour, lymphocytes, etc.) and then quantifying the co-localization of lymphocytes and tumour areas in a novel fashion. We investigate the prognostic significance of TILAb score on digitized WSIs of Hematoxylin and Eosin (H&E) stained slides of OSCC patients. Our deep learning based tissue segmentation achieves high accuracy of 96.31%, which paves the way for reliable downstream analysis. We show that the TILAb score is a strong prognostic indicator (p = 0.0006) of disease free survival (DFS) on our OSCC test cohort. The automated TILAb score has a significantly higher prognostic value than the manual TIL score (p = 0.0024). In summary, the proposed TILAb score is a digital biomarker which is based on more accurate classification of tumour and lymphocytic regions, is motivated by the biological definition of TILs as tumour infiltrating lymphocytes, with the added advantages of objective and reproducible quantification