Deep learning in histopathology: the path to the clinic
Abstract Machine learning techniques have great potential to improve medical diagnostics,
offering ways to improve accuracy, reproducibility and speed, and to ease workloads for …
offering ways to improve accuracy, reproducibility and speed, and to ease workloads for …
Deep neural network models for computational histopathology: A survey
CL Srinidhi, O Ciga, AL Martel - Medical image analysis, 2021 - Elsevier
Histopathological images contain rich phenotypic information that can be used to monitor
underlying mechanisms contributing to disease progression and patient survival outcomes …
underlying mechanisms contributing to disease progression and patient survival outcomes …
Scaling vision transformers to gigapixel images via hierarchical self-supervised learning
Abstract Vision Transformers (ViTs) and their multi-scale and hierarchical variations have
been successful at capturing image representations but their use has been generally …
been successful at capturing image representations but their use has been generally …
[HTML][HTML] Self supervised contrastive learning for digital histopathology
O Ciga, T Xu, AL Martel - Machine Learning with Applications, 2022 - Elsevier
Unsupervised learning has been a long-standing goal of machine learning and is especially
important for medical image analysis, where the learning can compensate for the scarcity of …
important for medical image analysis, where the learning can compensate for the scarcity of …
Cancer diagnosis using deep learning: a bibliographic review
In this paper, we first describe the basics of the field of cancer diagnosis, which includes
steps of cancer diagnosis followed by the typical classification methods used by doctors …
steps of cancer diagnosis followed by the typical classification methods used by doctors …
[HTML][HTML] Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images
Beyond sample curation and basic pathologic characterization, the digitized H&E-stained
images of TCGA samples remain underutilized. To highlight this resource, we present …
images of TCGA samples remain underutilized. To highlight this resource, we present …
DigestPath: A benchmark dataset with challenge review for the pathological detection and segmentation of digestive-system
Examination of pathological images is the golden standard for diagnosing and screening
many kinds of cancers. Multiple datasets, benchmarks, and challenges have been released …
many kinds of cancers. Multiple datasets, benchmarks, and challenges have been released …
Hybrid AI-assistive diagnostic model permits rapid TBS classification of cervical liquid-based thin-layer cell smears
Technical advancements significantly improve earlier diagnosis of cervical cancer, but
accurate diagnosis is still difficult due to various factors. We develop an artificial intelligence …
accurate diagnosis is still difficult due to various factors. We develop an artificial intelligence …
Interpretation and visualization techniques for deep learning models in medical imaging
DT Huff, AJ Weisman, R Jeraj - Physics in Medicine & Biology, 2021 - iopscience.iop.org
Deep learning (DL) approaches to medical image analysis tasks have recently become
popular; however, they suffer from a lack of human interpretability critical for both increasing …
popular; however, they suffer from a lack of human interpretability critical for both increasing …
Breast cancer detection, segmentation and classification on histopathology images analysis: a systematic review
R Krithiga, P Geetha - Archives of Computational Methods in Engineering, 2021 - Springer
Digital pathology represents a major evolution in modern medicine. Pathological
examinations constitute the standard in medical protocols and the law, and call for specific …
examinations constitute the standard in medical protocols and the law, and call for specific …