A ViT-AMC network with adaptive model fusion and multiobjective optimization for interpretable laryngeal tumor grading from histopathological images
The tumor grading of laryngeal cancer pathological images needs to be accurate and
interpretable. The deep learning model based on the attention mechanism-integrated …
interpretable. The deep learning model based on the attention mechanism-integrated …
Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles
J Barker, A Hoogi, A Depeursinge, DL Rubin - Medical image analysis, 2016 - Elsevier
Computerized analysis of digital pathology images offers the potential of improving clinical
care (eg automated diagnosis) and catalyzing research (eg discovering disease subtypes) …
care (eg automated diagnosis) and catalyzing research (eg discovering disease subtypes) …
A boosted Bayesian multiresolution classifier for prostate cancer detection from digitized needle biopsies
Diagnosis of prostate cancer (CaP) currently involves examining tissue samples for CaP
presence and extent via a microscope, a time-consuming and subjective process. With the …
presence and extent via a microscope, a time-consuming and subjective process. With the …
FABNet: fusion attention block and transfer learning for laryngeal cancer tumor grading in P63 IHC histopathology images
Laryngeal cancer tumor (LCT) grading is a challenging task in P63 Immunohistochemical
(IHC) histopathology images due to small differences between LCT levels in pathology …
(IHC) histopathology images due to small differences between LCT levels in pathology …
Histopathologic image processing: A review
Histopathologic Images (HI) are the gold standard for evaluation of some tumors. However,
the analysis of such images is challenging even for experienced pathologists, resulting in …
the analysis of such images is challenging even for experienced pathologists, resulting in …
ASI-DBNet: an adaptive sparse interactive resnet-vision transformer dual-branch network for the grading of brain cancer histopathological images
Brain cancer is the deadliest cancer that occurs in the brain and central nervous system, and
rapid and precise grading is essential to reduce patient suffering and improve survival …
rapid and precise grading is essential to reduce patient suffering and improve survival …
Cascaded discrimination of normal, abnormal, and confounder classes in histopathology: Gleason grading of prostate cancer
S Doyle, MD Feldman, N Shih, J Tomaszewski… - BMC …, 2012 - Springer
Background Automated classification of histopathology involves identification of multiple
classes, including benign, cancerous, and confounder categories. The confounder tissue …
classes, including benign, cancerous, and confounder categories. The confounder tissue …
Generative adversarial networks in medical image processing
M Gong, S Chen, Q Chen, Y Zeng… - Current pharmaceutical …, 2021 - ingentaconnect.com
Background: The emergence of generative adversarial networks (GANs) has provided new
technology and framework for the application of medical images. Specifically, a GAN …
technology and framework for the application of medical images. Specifically, a GAN …
A survey on automated cancer diagnosis from histopathology images
J Angel Arul Jothi, V Mary Anita Rajam - Artificial Intelligence Review, 2017 - Springer
Detecting cancer at an early stage is useful in better patient prognosis and treatment
planning. Even though there are several preliminary tests and non-invasive procedures that …
planning. Even though there are several preliminary tests and non-invasive procedures that …
Fuzzy diagnosis method for rotating machinery in variable rotating speed
H Wang, P Chen - IEEE Sensors Journal, 2010 - ieeexplore.ieee.org
In order to effectively diagnose faults for rotating machinery in the variable rotating speed, a
novel diagnosis method is proposed based on time-frequency analysis techniques, the …
novel diagnosis method is proposed based on time-frequency analysis techniques, the …