Deep learning in digital pathology image analysis: a survey
Deep learning (DL) has achieved state-of-the-art performance in many digital pathology
analysis tasks. Traditional methods usually require hand-crafted domain-specific features …
analysis tasks. Traditional methods usually require hand-crafted domain-specific features …
[HTML][HTML] Colon cancer diagnosis based on machine learning and deep learning: modalities and analysis techniques
The treatment and diagnosis of colon cancer are considered to be social and economic
challenges due to the high mortality rates. Every year, around the world, almost half a million …
challenges due to the high mortality rates. Every year, around the world, almost half a million …
Weakly supervised histopathology image segmentation with self-attention
Accurate segmentation in histopathology images at pixel-level plays a critical role in the
digital pathology workflow. The development of weakly supervised methods for …
digital pathology workflow. The development of weakly supervised methods for …
Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection
Background The large volume and suboptimal image quality of portable chest X-rays
(CXRs) as a result of the COVID-19 pandemic could post significant challenges for …
(CXRs) as a result of the COVID-19 pandemic could post significant challenges for …
Constrained deep weak supervision for histopathology image segmentation
In this paper, we develop a new weakly supervised learning algorithm to learn to segment
cancerous regions in histopathology images. This paper is under a multiple instance …
cancerous regions in histopathology images. This paper is under a multiple instance …
Weakly supervised histopathology cancer image segmentation and classification
Labeling a histopathology image as having cancerous regions or not is a critical task in
cancer diagnosis; it is also clinically important to segment the cancer tissues and cluster …
cancer diagnosis; it is also clinically important to segment the cancer tissues and cluster …
Detection and classification of cancer from microscopic biopsy images using clinically significant and biologically interpretable features
A framework for automated detection and classification of cancer from microscopic biopsy
images using clinically significant and biologically interpretable features is proposed and …
images using clinically significant and biologically interpretable features is proposed and …
Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles
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) …
[PDF][PDF] Automated cancer diagnosis based on histopathological images: a systematic survey
In traditional cancer diagnosis, pathologists examine biopsies to make diagnostic
assessments largely based on cell morphology and tissue distribution. However, this is …
assessments largely based on cell morphology and tissue distribution. However, this is …
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 …