Hospital Re-Admission Prediction Using Named Entity Recognition and Explainable Machine Learning.

Early hospital readmission refers to unplanned emergency admission of patients within 30 days of discharge. Predicting early readmission risk before discharge can help to reduce the cost of readmissions for hospitals and decrease the death rate for Intensive Care Unit patients. In this paper, we propose a novel approach for prediction of unplanned hospital readmissions using discharge notes from the MIMIC-III database.

Deep learning for report generation on chest X-ray images.

Medical imaging, specifically chest X-ray image analysis, is a crucial component of early disease detection and screening in healthcare. Deep learning techniques, such as convolutional neural networks (CNNs), have emerged as powerful tools for computer-aided diagnosis (CAD) in chest X-ray image analysis. These techniques have shown promising results in automating tasks such as classification, detection, and segmentation of abnormalities in chest X-ray images, with the potential to surpass human radiologists.

Explainable COVID-19 Detection Based on Chest X-rays Using an End-to-End RegNet Architecture.

COVID-19,which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is one of the worst pandemics in recent history. The identification of patients suspected to be infected with COVID-19 is becoming crucial to reduce its spread. We aimed to validate and test a deep learning model to detect COVID-19 based on chest X-rays.

A novel breast cancer detection architecture based on a CNN-CBR system for mammogram classification.

This paper presents a novel framework for breast cancer detection using mammogram images. The proposed solution aims to output an explainable classification from a mammogram image. The classification approach uses a Case-Based Reasoning system (CBR).

Vision Transformers for Lung Segmentation on CXR Images.

Accurate segmentation of the lungs in CXR images is the basis for an automated CXR image analysis system. It helps radiologists in detecting lung areas, subtle signs of disease and improving the diagnosis process for patients. However, precise semantic segmentation of lungs is considered a challenging case due to the presence of the edge rib cage, wide variation of lung shape, and lungs affected by diseases.

Deep Learning Methods for Chest Disease Detection Using Radiography Images.

X-ray images are the most widely used medical imaging modality. They are affordable, non-dangerous, accessible, and can be used to identify different diseases. Multiple computer-aided detection (CAD) systems using deep learning (DL) algorithms were recently proposed to support radiologists in identifying different diseases on medical images.

DCTable: A Dilated CNN with Optimizing Anchors for Accurate Table Detection.

With the widespread use of deep learning in leading systems, it has become the mainstream in the table detection field. Some tables are difficult to detect because of the likely figure layout or the small size. As a solution to the underlined problem, we propose a novel method, called DCTable, to improve Faster R-CNN for table detection.

A Review of Recent Advances in Deep Learning Models for Chest Disease Detection Using Radiography.

Chest X-ray radiography (CXR) is among the most frequently used medical imaging modalities. It has a preeminent value in the detection of multiple life-threatening diseases. Radiologists can visually inspect CXR images for the presence of diseases.

Applying Deep Learning for Breast Cancer Detection in Radiology.

Recent advances in deep learning have enhanced medical imaging research. Breast cancer is the most prevalent cancer among women, and many applications have been developed to improve its early detection. The purpose of this review is to examine how various deep learning methods can be applied to breast cancer screening workflows.

Explainable Vision Transformers and Radiomics for COVID-19 Detection in Chest X-rays.

The rapid spread of COVID-19 across the globe since its emergence has pushed many countries' healthcare systems to the verge of collapse. To restrict the spread of the disease and lessen the ongoing cost on the healthcare system, it is critical to appropriately identify COVID-19-positive individuals and isolate them as soon as possible. The primary COVID-19 screening test, RT-PCR, although accurate and reliable, has a long turn-around time.

Deep Learning and Transformer Approaches for UAV-Based Wildfire Detection and Segmentation.

Wildfires are a worldwide natural disaster causing important economic damages and loss of lives. Experts predict that wildfires will increase in the coming years mainly due to climate change. Early detection and prediction of fire spread can help reduce affected areas and improve firefighting.

Ocular Diseases Detection using Recent Deep Learning Techniques.

Early fundus screening is a cost-effective and efficient approach to reduce ophthalmic disease-related blindness in ophthalmology. Manual evaluation is time-consuming. Ophthalmic disease detection studies have shown interesting results thanks to the advancement in deep learning techniques, but the majority of them are limited to a single disease.

Automated Detection of COVID-19 Cases using Recent Deep Convolutional Neural Networks and CT images.

COVID-19 is an acute severe respiratory disease caused by a novel coronavirus SARS-CoV-2. After its first appearance in Wuhan (China), it spread rapidly across the world and became a pandemic. It had a devastating effect on everyday life, public health, and the world economy.

Melanoma Skin Cancer Detection Using Recent Deep Learning Models.

Melanoma is considered as one of the world's deadly cancers. This type of skin cancer will spread to other areas of the body if not detected at an early stage. Convolutional Neural Network (CNN) based classifiers are currently considered one of the most effective melanoma detection techniques.

Deep Learning and Binary Relevance Classification of Multiple Diseases using Chest X-Ray images.

Disease detection using chest X-ray (CXR) images is one of the most popular radiology methods to diagnose diseases through a visual inspection of abnormal symptoms in the lung region. A wide variety of diseases such as pneumonia, heart failure and lung cancer can be detected using CXRs. Although CXRs can show the symptoms of a variety of diseases, detecting and manually classifying those diseases can be difficult and time-consuming adding to clinicians' work burden.

Predicting Cancer Types From miRNA Stem-loops Using Deep Learning.

With cancer being one of the main remaining challenges of modern medicine, a lot of effort is put towards oncology research. Since early diagnosis is a highly important factor for the treatment of many types of cancer, screening tests have become a popular research subject. Technical and technological advances have brought down the price of genome sequencing and have led to an increase in understanding the relationship between DNA, RNA and tumor sites.

Explainable Diabetic Retinopathy using EfficientNET.

Diabetic retinopathy (DR) is a medical condition due to diabetes mellitus that can damage the patient retina and cause blood leaks. This condition can cause different symptoms from mild vision problems to complete blindness if it is not timely treated. In this work, we propose the use of a deep learning architecture based on a recent convolutional neural network called EfficientNet to detect referable diabetic retinopathy (RDR) and vision-threatening DR.

Evaluating Deep Learning Algorithms in Pulmonary Nodule Detection.

Lung cancer is considered the deadliest cancer worldwide. In order to detect it, radiologists need to inspect multiple Computed Tomography (CT) scans. This task is tedious and time consuming.

Explainable end-to-end deep learning for diabetic retinopathy detection across multiple datasets.

: Diabetic retinopathy (DR) is characterized by retinal lesions affecting people having diabetes for several years. It is one of the leading causes of visual impairment worldwide. To diagnose this disease, ophthalmologists need to manually analyze retinal fundus images.