AIMC Topic: Deep Learning

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PediMS: A Pediatric Multiple Sclerosis Lesion Segmentation Dataset.

Scientific data
Multiple Sclerosis (MS) is a chronic autoimmune disease that primarily affects the central nervous system and is predominantly diagnosed in adults, making pediatric cases rare and underrepresented in medical research. This paper introduces the first ...

A transformer-based network with second-order pooling for motor imagery EEG classification.

Journal of neural engineering
. Electroencephalography (EEG) signals can reflect motor intention signals in the brain. In recent years, motor imagery (MI) based brain-computer interfaces (BCIs) have attracted the attention of neuroinformatics researchers. Numerous deep learning m...

In-silico CT simulations of deep learning generated heterogeneous phantoms.

Biomedical physics & engineering express
Current virtual imaging phantoms primarily emphasize geometric accuracy of anatomical structures. However, to enhance realism, it is also important to incorporate intra-organ detail. Because biological tissues are heterogeneous in composition, virtua...

BIScreener: enhancing breast cancer ultrasound diagnosis through integrated deep learning with interpretability.

Analytical methods : advancing methods and applications
Breast cancer is the leading cause of death among women worldwide, and early detection through the standardized BI-RADS framework helps physicians assess the risk of malignancy and guide appropriate diagnostic and treatment decisions. In this study, ...

Decoding tissue complexity: multiscale mapping of chemistry-structure-function relationships through advanced visualization technologies.

Journal of materials chemistry. B
Comprehensively acquiring biological tissue information is pivotal for advancing our understanding of biological systems, elucidating disease mechanisms, and developing innovative clinical strategies. Biological tissues, as nature's archetypal biomat...

Multi-scale time series prediction model based on deep learning and its application.

PloS one
Time series prediction is a widely used key technology, and traffic flow prediction is its typical application scenario. Traditional time series prediction models such as LSTM (Long Short- Term Memory) and CNN (Convolution Neural Network)-based model...

Nuclei segmentation and classification from histopathology images using federated learning for end-edge platform.

PloS one
Accurate nuclei segmentation and classification in histology images are critical for cancer detection but remain challenging due to color inconsistency, blurry boundaries, and overlapping nuclei. Manual segmentation is time-consuming and labor-intens...

Mitosis detection in histopathological images using customized deep learning and hybrid optimization algorithms.

PloS one
Identifying mitosis is crucial for cancer diagnosis, but accurate detection remains difficult because of class imbalance and complex morphological variations in histopathological images. To overcome this challenge, we propose a Customized Deep Learni...

Metaverse-based deep learning framework for coronary artery stenosis classification using Monte Carlo Dropout-based ResNet-152.

Computers in biology and medicine
Metaverse offers an immersive healthcare platform that combines virtual reality (VR) and artificial intelligence (AI), providing a new approach to medical diagnostics. However, difficulties such as inadequate spatial resolution, uncertainty managemen...

Artificial intelligence in prostate cancer.

Chinese medical journal
Prostate cancer (PCa) ranks as the second most prevalent malignancy among men worldwide. Early diagnosis, personalized treatment, and prognosis prediction of PCa play a crucial role in improving patients' survival rates. The advancement of artificial...