A comprehensive review of the deep learning-based tumor analysis approaches in histopathological images: segmentation, classification and multi-learning tasks
Medical Imaging has become a vital technique that has been embraced in the diagnosis and
treatment process of cancer. Histopathological slides, which microscopically examine the …
treatment process of cancer. Histopathological slides, which microscopically examine the …
Shortcomings and areas for improvement in digital pathology image segmentation challenges
Digital pathology image analysis challenges have been organised regularly since 2010,
often with events hosted at major conferences and results published in high-impact journals …
often with events hosted at major conferences and results published in high-impact journals …
[HTML][HTML] POST-IVUS: A perceptual organisation-aware selective transformer framework for intravascular ultrasound segmentation
Intravascular ultrasound (IVUS) is recommended in guiding coronary intervention. The
segmentation of coronary lumen and external elastic membrane (EEM) borders in IVUS …
segmentation of coronary lumen and external elastic membrane (EEM) borders in IVUS …
Sketch-supervised histopathology tumour segmentation: Dual CNN-transformer with global normalised CAM
Deep learning methods are frequently used in segmenting histopathology images with high-
quality annotations nowadays. Compared with well-annotated data, coarse, scribbling-like …
quality annotations nowadays. Compared with well-annotated data, coarse, scribbling-like …
Toothpix: Pixel-level tooth segmentation in panoramic x-ray images based on generative adversarial networks
Accurate tooth segmentation in panoramic X-ray images is an essential stage before clinical
surgery. This paper presents a deep segmentation network ToothPix, which leverages …
surgery. This paper presents a deep segmentation network ToothPix, which leverages …
Learning to segment images with classification labels
O Ciga, AL Martel - Medical Image Analysis, 2021 - Elsevier
Two of the most common tasks in medical imaging are classification and segmentation.
Either task requires labeled data annotated by experts, which is scarce and expensive to …
Either task requires labeled data annotated by experts, which is scarce and expensive to …
Transformer-based semantic segmentation and CNN network for detection of histopathological lung cancer
The lungs are a very important organ in a human. Any abnormality in the lungs ultimately
affects the whole body. Pulmonary nodules-initiated lung cancer that is very small in size …
affects the whole body. Pulmonary nodules-initiated lung cancer that is very small in size …
Structure-aware scale-adaptive networks for cancer segmentation in whole-slide images
Cancer segmentation in whole-slide images is a fundamental step for viable tumour burden
estimation, which is of great value for cancer assessment. However, factors like vague …
estimation, which is of great value for cancer assessment. However, factors like vague …
Computational Pathology for Brain Disorders
G Jiménez, D Racoceanu - Machine Learning for Brain Disorders, 2023 - Springer
Noninvasive brain imaging techniques allow understanding the behavior and macro
changes in the brain to determine the progress of a disease. However, computational …
changes in the brain to determine the progress of a disease. However, computational …
Addressing the data annotation bottleneck in breast digital pathology
O Ciga - 2021 - search.proquest.com
Early diagnosis and targeted therapies are priorities in the treatment of cancer.
Advancements such as whole-slide scanning of a tissue sample can help experts assess …
Advancements such as whole-slide scanning of a tissue sample can help experts assess …