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 …
DenseRes-Unet: Segmentation of overlapped/clustered nuclei from multi organ histopathology images
Cancer is the second deadliest disease globally that can affect any human body organ.
Early detection of cancer can increase the chances of survival in humans. Morphometric …
Early detection of cancer can increase the chances of survival in humans. Morphometric …
[HTML][HTML] Multi-scale fully convolutional neural networks for histopathology image segmentation: from nuclear aberrations to the global tissue architecture
Histopathologic diagnosis relies on simultaneous integration of information from a broad
range of scales, ranging from nuclear aberrations (≈ O (0.1 μ m)) through cellular structures …
range of scales, ranging from nuclear aberrations (≈ O (0.1 μ m)) through cellular structures …
A review of machine learning approaches, challenges and prospects for computational tumor pathology
Computational pathology is part of precision oncology medicine. The integration of high-
throughput data including genomics, transcriptomics, proteomics, metabolomics, pathomics …
throughput data including genomics, transcriptomics, proteomics, metabolomics, pathomics …
MaxViT-UNet: Multi-axis attention for medical image segmentation
Since their emergence, Convolutional Neural Networks (CNNs) have made significant
strides in medical image analysis. However, the local nature of the convolution operator may …
strides in medical image analysis. However, the local nature of the convolution operator may …
BrcaSeg: A Deep Learning Approach for Tissue Quantification and Genomic Correlations of Histopathological Images
Epithelial and stromal tissues are components of the tumor microenvironment and play a
major role in tumor initiation and progression. Distinguishing stroma from epithelial tissues is …
major role in tumor initiation and progression. Distinguishing stroma from epithelial tissues is …
Dilated and soft attention‐guided convolutional neural network for breast cancer histology images classification
Y Zhong, Y Piao, G Zhang - Microscopy Research and …, 2022 - Wiley Online Library
Breast cancer is one of the most common types of cancer in women, and histopathological
imaging is considered the gold standard for its diagnosis. However, the great complexity of …
imaging is considered the gold standard for its diagnosis. However, the great complexity of …
Unsupervised tumor characterization via conditional generative adversarial networks
Grading for cancer, based upon the degree of cancer differentiation, plays a major role in
describing the characteristics and behavior of the cancer and determining treatment plan for …
describing the characteristics and behavior of the cancer and determining treatment plan for …
Classification of cervical precursor lesions via local histogram and cell morphometric features
Cervical squamous intra-epithelial lesions (SIL) are precursor cancer lesions and their
diagnosis is important because patients have a chance to be cured before cancer develops …
diagnosis is important because patients have a chance to be cured before cancer develops …
Ranking loss: a ranking-based deep neural network for colorectal cancer grading in pathology images
TT Le Vuong, K Kim, B Song, JT Kwak - … 1, 2021, Proceedings, Part VIII 24, 2021 - Springer
In digital pathology, cancer grading has been widely studied by utilizing hand-crafted
features and advanced machine learning and deep learning methods. In most of such …
features and advanced machine learning and deep learning methods. In most of such …