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Lung-PNet: an automated deep learning model for the diagnosis of invasive adenocarcinoma in pure ground-glass nodules on chest CT
K Qi, K Wang, X Wang, YD Zhang, G Lin… - American Journal of …, 2024 - ajronline.org
BACKGROUND. Pure ground-glass nodules (pGGNs) on chest CT representing invasive
adenocarcinoma (IAC) warrant lobectomy with lymph node resection. For pGGNs …
adenocarcinoma (IAC) warrant lobectomy with lymph node resection. For pGGNs …
Automated placental abruption identification using semantic segmentation, quantitative features, SVM, ensemble and multi-path CNN
V Asadpour, EJ Puttock, D Getahun, MJ Fassett, F **e - Heliyon, 2023 - cell.com
The placenta is a fundamental organ throughout the pregnancy and the fetus' health is
closely related to its proper function. Because of the importance of the placenta, any …
closely related to its proper function. Because of the importance of the placenta, any …
Enhancing deep learning based classifiers with inpainting anatomical side markers (L/R markers) for multi-center trials
Background and objective The protocol for placing anatomical side markers (L/R markers) in
chest radiographs varies from one hospital or department to another. However, the markers …
chest radiographs varies from one hospital or department to another. However, the markers …
Prediction of type II diabetes onset with computed tomography and electronic medical records
Type II diabetes mellitus (T2DM) is a significant public health concern with multiple known
risk factors (eg, body mass index (BMI), body fat distribution, glucose levels). Improved …
risk factors (eg, body mass index (BMI), body fat distribution, glucose levels). Improved …
Quantitative radiomic features from computed tomography can predict pancreatic cancer up to 36 months before diagnosis
W Chen, Y Zhou, V Asadpour, RA Parker… - Clinical and …, 2023 - journals.lww.com
METHODS: Adults 18 years and older diagnosed with PDAC in 2008–2018 were identified.
Their CT scans 3 months–3 years before the diagnosis date were matched to up to 2 scans …
Their CT scans 3 months–3 years before the diagnosis date were matched to up to 2 scans …
[HTML][HTML] Deep learning to detect pancreatic cystic lesions on abdominal computed tomography scans: Development and validation study
MM Duh, N Torra-Ferrer, M Riera-Marín, D Cumelles… - JMIR AI, 2023 - ai.jmir.org
Background Pancreatic cystic lesions (PCLs) are frequent and underreported incidental
findings on computed tomography (CT) scans and can evolve to pancreatic cancer—the …
findings on computed tomography (CT) scans and can evolve to pancreatic cancer—the …
Advancing Medical Image Segmentation: Morphology-Driven Learning with Diffusion Transformer
S Kang, J Song, J Kim - arxiv preprint arxiv:2408.00347, 2024 - arxiv.org
Understanding the morphological structure of medical images and precisely segmenting the
region of interest or abnormality is an important task that can assist in diagnosis. However …
region of interest or abnormality is an important task that can assist in diagnosis. However …
[HTML][HTML] Machine Learning-Driven Radiomics Analysis for Distinguishing Mucinous and Non-Mucinous Pancreatic Cystic Lesions: A Multicentric Study
N Torra-Ferrer, MM Duh, Q Grau-Ortega… - Journal of …, 2025 - mdpi.com
The increasing use of high-resolution cross-sectional imaging has significantly enhanced
the detection of pancreatic cystic lesions (PCLs), including pseudocysts and neoplastic …
the detection of pancreatic cystic lesions (PCLs), including pseudocysts and neoplastic …
A convolutional neural network-based auto-segmentation pipeline for breast cancer imaging
Medical imaging is crucial for the detection and diagnosis of breast cancer. Artificial
intelligence and computer vision have rapidly become popular in medical image analyses …
intelligence and computer vision have rapidly become popular in medical image analyses …
[HTML][HTML] WindowNet: Learnable Windows for Chest X-ray Classification
Public chest X-ray (CXR) data sets are commonly compressed to a lower bit depth to reduce
their size, potentially hiding subtle diagnostic features. In contrast, radiologists apply a …
their size, potentially hiding subtle diagnostic features. In contrast, radiologists apply a …