AIMC Topic: Deep Learning

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Assessment of different U-Net backbones in segmenting colorectal adenocarcinoma from H&E histopathology.

Pathology, research and practice
Adenocarcinoma, the most prevalent type of colorectal cancer, makes up roughly 95 % of all cases and is associated with a notably high mortality rate. Owing to the various risk factors which might include personal choices and habits or genetic factor...

Pretrained Deep Neural Network Kin-SiM for Single-Molecule FRET Trace Idealization.

The journal of physical chemistry. B
Single-molecule fluorescence resonance energy transfer (smFRET) has emerged as a pivotal technique for probing biomolecular dynamics over time at nanometer scales. Quantitative analyses of smFRET time traces remain challenging due to confounding fact...

Fluorescence Lifetime Endoscopy with a Nanosecond Time-Gated CAPS Camera with IRF-Free Deep Learning Method.

Sensors (Basel, Switzerland)
Fluorescence imaging has been widely used in fields like (pre)clinical imaging and other domains. With advancements in imaging technology and new fluorescent labels, fluorescence lifetime imaging is gradually gaining recognition. Our research departm...

Effect of feedback-integrated reflection, on deep learning of undergraduate medical students in a clinical setting.

BMC medical education
BACKGROUND: Reflection fosters self-regulated learning by enabling learners to critically evaluate their performance, identify gaps, and make plans to improve. Feedback, in turn, provides external insights that complement reflection, helping learners...

Intelligent in-cell electrophysiology: Reconstructing intracellular action potentials using a physics-informed deep learning model trained on nanoelectrode array recordings.

Nature communications
Intracellular electrophysiology is essential in neuroscience, cardiology, and pharmacology for studying cells' electrical properties. Traditional methods like patch-clamp are precise but low-throughput and invasive. Nanoelectrode Arrays (NEAs) offer ...

Deep learning-based assessment of missense variants in the gene presented with bilateral congenital cataract.

BMJ open ophthalmology
OBJECTIVE: We compared the protein structure and pathogenicity of clinically relevant variants of the gene with AlphaFold2 (AF2), Alpha Missense (AM), and ThermoMPNN for the first time.

E-DFu-Net: An efficient deep convolutional neural network models for Diabetic Foot Ulcer classification.

Biomolecules & biomedicine
The Diabetic Foot Ulcer (DFU) is a severe complication that affects approximately 33% of diabetes patients globally, often leading to limb amputation if not detected early. This study introduces an automated approach for identifying and classifying D...

A networked station system for high-resolution wind nowcasting in air traffic operations: A data-augmented deep learning approach.

PloS one
This study introduces a high-resolution wind nowcasting model designed for aviation applications at Madeira International Airport, a location known for its complex wind patterns. By using data from a network of six meteorological stations and deep le...

Enhancing the visual environment of urban coastal roads through deep learning analysis of street-view images: A perspective of aesthetic and distinctiveness.

PloS one
Urban waterfront areas, which are essential natural resources and highly perceived public areas in cities, play a crucial role in enhancing urban environment. This study integrates deep learning with human perception data sourced from street view ima...

Integration of Deep Learning and Sub-regional Radiomics Improves the Prediction of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer Patients.

Academic radiology
RATIONALE AND OBJECTIVES: The precise prediction of response to neoadjuvant chemoradiotherapy is crucial for tailoring perioperative treatment in patients diagnosed with locally advanced rectal cancer (LARC). This retrospective study aims to develop ...