Multiple sclerosis lesion segmentation from brain MRI using U-Net based on wavelet pooling.

Purpose: The purpose of this work is to segment multiple sclerosis (MS) lesions in magnetic resonance imaging (MRI) images, in which lesions in different sizes are segmented with appropriate accuracy. Automated segmentation as a powerful tool can assist professionals to increase the accuracy of disease diagnosis and its level of progression.

Methods: We present a deep neural network based on the U-Net architecture in which wavelet transform-based pooling replaces max pooling.

Design and synthesis of novel 4-thiazolidinone derivatives with promising anti-breast cancer activity: Synthesis, characterization, in vitro and in vivo results.

Novel lead compounds as anticancer agents with the ability to circumvent emerging drug resistance have recently gained a great deal of interest. Thiazolidinones are among such compounds with well-established biological activity in the field of oncology. Here, we designed, synthesized and characterized a series of thiazolidinone structures (8a-8k).

AdaptAhead Optimization Algorithm for Learning Deep CNN Applied to MRI Segmentation.

Deep learning is one of the subsets of machine learning that is widely used in artificial intelligence (AI) field such as natural language processing and machine vision. The deep convolution neural network (DCNN) extracts high-level concepts from low-level features and it is appropriate for large volumes of data. In fact, in deep learning, the high-level concepts are defined by low-level features.

An Efficient Implementation of Deep Convolutional Neural Networks for MRI Segmentation.

Image segmentation is one of the most common steps in digital image processing, classifying a digital image into different segments. The main goal of this paper is to segment brain tumors in magnetic resonance images (MRI) using deep learning. Tumors having different shapes, sizes, brightness and textures can appear anywhere in the brain.

Arylhydrazone derivatives of naproxen as new analgesic and anti-inflammatory agents: Design, synthesis and molecular docking studies.

A series of new arylidenehydrazone derivatives of naproxen were synthesized and evaluated for their analgesic and anti-inflammatory activities. Some of the synthesized analogues showed comparable activities when compared against naproxen for their analgesic and anti-inflammatory properties. 2-(6-methoxy-2-naphthyl)-N'-[(pyridine-4-yl)methylene]propanoic acid hydrazide 4j was found to be the most active analgesic agent.