Deep learning for medical image-based cancer diagnosis
Simple Summary Deep learning has succeeded greatly in medical image-based cancer
diagnosis. To help readers better understand the current research status and ideas, this …
diagnosis. To help readers better understand the current research status and ideas, this …
UTRNet: An unsupervised time-distance-guided convolutional recurrent network for change detection in irregularly collected images
Change detection in time series is among the most critical problems in Earth monitoring and
attracts extensive attention in the remote sensing community. The task is, however, nontrivial …
attracts extensive attention in the remote sensing community. The task is, however, nontrivial …
Systematic review for lung cancer detection and lung nodule classification: Taxonomy, challenges, and recommendation future works
Nowadays, lung cancer is one of the most dangerous diseases that require early diagnosis.
Artificial intelligence has played an essential role in the medical field in general and in …
Artificial intelligence has played an essential role in the medical field in general and in …
Deep learning for predicting COVID-19 malignant progression
C Fang, S Bai, Q Chen, Y Zhou, L **a, L Qin… - Medical image …, 2021 - Elsevier
As COVID-19 is highly infectious, many patients can simultaneously flood into hospitals for
diagnosis and treatment, which has greatly challenged public medical systems. Treatment …
diagnosis and treatment, which has greatly challenged public medical systems. Treatment …
Glim-net: chronic glaucoma forecast transformer for irregularly sampled sequential fundus images
Chronic Glaucoma is an eye disease with progressive optic nerve damage. It is the second
leading cause of blindness after cataract and the first leading cause of irreversible …
leading cause of blindness after cataract and the first leading cause of irreversible …
The synergy between deep learning and organs-on-chips for high-throughput drug screening: a review
Organs-on-chips (OoCs) are miniature microfluidic systems that have arguably become a
class of advanced in vitro models. Deep learning, as an emerging topic in machine learning …
class of advanced in vitro models. Deep learning, as an emerging topic in machine learning …
[HTML][HTML] Prediction of future imagery of lung nodule as growth modeling with follow-up computed tomography scans using deep learning: a retrospective cohort study
Background Risk prediction models of lung nodules have been built to alleviate the heavy
interpretative burden on clinicians. However, the malignancy scores output by those models …
interpretative burden on clinicians. However, the malignancy scores output by those models …
Reducing uncertainty in cancer risk estimation for patients with indeterminate pulmonary nodules using an integrated deep learning model
Objective Patients with indeterminate pulmonary nodules (IPN) with an intermediate to a
high probability of lung cancer generally undergo invasive diagnostic procedures. Chest …
high probability of lung cancer generally undergo invasive diagnostic procedures. Chest …
[HTML][HTML] Time-distance vision transformers in lung cancer diagnosis from longitudinal computed tomography
Features learned from single radiologic images are unable to provide information about
whether and how much a lesion may be changing over time. Time-dependent features …
whether and how much a lesion may be changing over time. Time-dependent features …
[PDF][PDF] Evaluating the readability of English instructional materials in Pakistani Universities: A deep learning and statistical approach
M Saqlain - Education Science and Management, 2023 - library.acadlore.com
In educational settings of Pakistan, where English is utilized as the primary medium of
instruction but not as an official language, the assessment of instructional text readability is …
instruction but not as an official language, the assessment of instructional text readability is …