AIMC Topic: Deep Learning

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Characterization of Gastrodiae Rhizoma from different geographical origins by HS-GC-IMS and authenticity identification combined with deep learning.

Journal of chromatography. A
Given the growing demand for Gastrodiae Rhizoma (GR), it is important to establish a strategy for origin authentication and adulteration screening to ensure the high-quality products in the market. Currently, efficient and dependable methods for iden...

Role of artificial intelligence in medical image analysis.

Chinese medical journal
With the emergence of deep learning techniques based on convolutional neural networks, artificial intelligence (AI) has driven transformative developments in the field of medical image analysis. Recently, large language models (LLMs) such as ChatGPT ...

Deep Learning in neuroimaging for neurodegenerative diseases: State-of-the art, Challenges, and Opportunities.

Journal of the neurological sciences
Neuroimaging is commonly used to diagnose neurodegenerative diseases (NDDs), providing crucial insights into brain changes before clinical symptoms manifest. Deep learning (DL) for neuroimaging can improve early diagnosis and disease monitoring. Clin...

High-acceleration pancreatobiliary MRI with deep learning-based super-resolution reconstruction for evaluating presumed pancreatic intraductal papillary mucinous neoplasm.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: To evaluate the feasibility and diagnostic utility of a deep learning (DL)-based super-resolution (SR) reconstruction algorithm applied to pancreatobiliary MRI for assessing pancreatic intraductal papillary mucinous neoplasms (IPMNs).

Kideraspa: designing variants of staphylococcal protein a based on a diffusion model with kidera factors.

Journal of computer-aided molecular design
The interaction between staphylococcal protein A (SpA) and human immunoglobulin G (IgG) is pivotal in treating diseases such as cancer, inflammation, infections, and autoimmune disorders. However, acquiring natural SpA variants is labor-intensive, tr...

MobileDANet integrating transfer learning and dynamic attention for classifying multi target histopathology images with explainable AI.

Scientific reports
Cancer is a life-threatening disease that affects several human lives all over the world. The classification of cancer severities utilizing histopathological images is vital for effective and timely diagnosis. This always creates a demandable require...

Prediction of advanced chronic kidney disease through retinal fundus images by deep learning.

Scientific reports
This study was developed and evaluated deep learning model for detecting chronic kidney disease (CKD) by retinal fundus images. This study included 42,963 clinical visits from 17,442 patients who underwent retinal fundus examination between October 1...

A deep learning based framework for music-synchronized dance choreography with pose quantization and motion prediction for activity recognition.

Scientific reports
The ability to generate dynamic, expressive dance routines that adapt to various musical compositions has broad applications in activity recognition, performance arts, entertainment, virtual reality, and interactive media, offering new avenues for cr...

Privacy preservation in diabetic disease prediction using federated learning based on efficient cross stage recurrent model.

Scientific reports
Diabetic retinopathy (DR) is a major problemfor the diabetes patients that makes a serious threat to vision and causes the irreversible blindness if not diagnosed and treated early. Conventional deep learning-based approaches designed for DR detectio...

Multi-institutional validation of AI models for classifying urothelial neoplasms in digital pathology.

Scientific reports
This study proposes a deep learning approach for classifying normal, noninvasive, and invasive urothelial neoplasms via digitized histopathologicalimages. Despite many artificial intelligence (AI) models for cancer diagnosis, few focus on bladder les...