AIMC Topic: Deep Learning

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TRUSWorthy: toward clinically applicable deep learning for confident detection of prostate cancer in micro-ultrasound.

International journal of computer assisted radiology and surgery
PURPOSE: While deep learning methods have shown great promise in improving the effectiveness of prostate cancer (PCa) diagnosis by detecting suspicious lesions from trans-rectal ultrasound (TRUS), they must overcome multiple simultaneous challenges. ...

T2-weighted imaging of rectal cancer using a 3D fast spin echo sequence with and without deep learning reconstruction: A reader study.

Journal of applied clinical medical physics
PURPOSE: To compare image quality and clinical utility of a T2-weighted (T2W) 3-dimensional (3D) fast spin echo (FSE) sequence using deep learning reconstruction (DLR) versus conventional reconstruction for rectal magnetic resonance imaging (MRI).

Automated segmentation by SCA-UNet can be directly used for radiomics diagnosis of thymic epithelial tumors.

European journal of radiology
BACKGROUND: Automatic segmentation of thymic lesions in preoperative computed tomography (CT) images is crucial for accurate diagnosis but remains time-consuming. Although UNet is widely used in medical imaging, its performance is limited by the inhe...

Physics-informed neural networks for enhanced reference evapotranspiration estimation in Morocco: Balancing semi-physical models and deep learning.

Chemosphere
Reference evapotranspiration (ETo) is essential for agricultural water management, crop productivity, and irrigation systems. The Penman-Monteith (PM) equation is the standard method for estimating ETo, but its data-intensive nature makes it impracti...

Integrating manual preprocessing with automated feature extraction for improved rodent seizure classification.

Epilepsy & behavior : E&B
HYPOTHESIS/OBJECTIVE: Rodent models of epilepsy can help with the search for more effective drug candidates or neuromodulatory therapies. Yet, preclinical screening of candidate options for anti-epileptic drugs (AED) using rodent models may require h...

Toward deep learning sequence-structure co-generation for protein design.

Current opinion in structural biology
Deep generative models that learn from the distribution of natural protein sequences and structures may enable the design of new proteins with valuable functions. While the majority of today's models focus on generating either sequences or structures...

RNA structure prediction using deep learning - A comprehensive review.

Computers in biology and medicine
In computational biology, accurate RNA structure prediction offers several benefits, including facilitating a better understanding of RNA functions and RNA-based drug design. Implementing deep learning techniques for RNA structure prediction has led ...

Addressing imbalance in health data: Synthetic minority oversampling using deep learning.

Computers in biology and medicine
Class imbalances in healthcare data, characterized by a disproportionate number of positive cases compared to negative ones, can lead to biased machine learning models that favor the majority class. Ensuring good performance across all classes is cru...

A multi-task self-supervised approach for mass detection in automated breast ultrasound using double attention recurrent residual U-Net.

Computers in biology and medicine
Breast cancer is the most common and lethal cancer among women worldwide. Early detection using medical imaging technologies can significantly improve treatment outcomes. Automated breast ultrasound, known as ABUS, offers more advantages compared to ...

Leveraging Radiomics and Hybrid Quantum-Classical Convolutional Networks for Non-Invasive Detection of Microsatellite Instability in Colorectal Cancer.

Molecular imaging and biology
PURPOSE: The goal of this study is to create a novel framework for identifying MSI status in colorectal cancer using advanced radiomics and deep learning strategies, aiming to enhance clinical decision-making and improve patient outcomes in oncology.