Toward automated detection of microbleeds with anatomical scale localization using deep learning.

Cerebral Microbleeds (CMBs) are chronic deposits of small blood products in the brain tissues, which have explicit relation to various cerebrovascular diseases depending on their anatomical location, including cognitive decline, intracerebral hemorrhage, and cerebral infarction. However, manual detection of CMBs is a time consuming and error-prone process because of their sparse and tiny structural properties. The detection of CMBs is commonly affected by the presence of many CMB mimics that cause a high false-positive rate (FPR), such as calcifications and pial vessels.

A Unified Multi-Task Learning Model with Joint Reverse Optimization for Simultaneous Skin Lesion Segmentation and Diagnosis.

Classifying and segmenting skin cancer represent pivotal objectives for automated diagnostic systems that utilize dermoscopy images. However, these tasks present significant challenges due to the diverse shape variations of skin lesions and the inherently fuzzy nature of dermoscopy images, including low contrast and the presence of artifacts. Given the robust correlation between the classification of skin lesions and their segmentation, we propose that employing a combined learning method holds the promise of considerably enhancing the performance of both tasks.

Electroretinogram Analysis Using a Short-Time Fourier Transform and Machine Learning Techniques.

Electroretinography (ERG) is a non-invasive method of assessing retinal function by recording the retina's response to a brief flash of light. This study focused on optimizing the ERG waveform signal classification by utilizing Short-Time Fourier Transform (STFT) spectrogram preprocessing with a machine learning (ML) decision system. Several window functions of different sizes and window overlaps were compared to enhance feature extraction concerning specific ML algorithms.

Deep Network Regularization for Phase-based Magnetic Resonance Electrical Properties Tomography with Stein's Unbiased Risk Estimator.

Magnetic resonance imaging (MRI) can extract the tissue conductivity values from in vivo data using the so-called phase-based magnetic resonance electrical properties tomography (MR-EPT). However, this procedure suffers from noise amplification caused by the use of the Laplacian operator. To counter this issue, we propose a novel preprocessing denoiser for magnetic resonance transceive phase images, operating in an unsupervised manner.

Semantic and traditional feature fusion for software defect prediction using hybrid deep learning model.

Software defect prediction aims to find a reliable method for predicting defects in a particular software project and assisting software engineers in allocating limited resources to release high-quality software products. While most earlier research has concentrated on employing traditional features, current methodologies are increasingly directed toward extracting semantic features from source code. Traditional features often fall short in identifying semantic differences within programs, differences that are essential for the development of reliable and effective prediction models.