Artificial intelligence in computational pathology–challenges and future directions
The field of digital histopathology has seen incredible growth in recent years. Digital
pathology is becoming a relevant tool in healthcare, industrial and research sectors to …
pathology is becoming a relevant tool in healthcare, industrial and research sectors to …
Divide-and-rule: self-supervised learning for survival analysis in colorectal cancer
With the long-term rapid increase in incidences of colorectal cancer (CRC), there is an
urgent clinical need to improve risk stratification. The conventional pathology report is …
urgent clinical need to improve risk stratification. The conventional pathology report is …
An advanced diagnostic ColoRectalCADx utilises CNN and unsupervised visual explanations to discover malignancies
Colorectal cancer (CRC) is one of the most lethal kinds of cancer, so early detection is
critical. Three datasets, namely CNN transfer learning with discrete wavelet transform …
critical. Three datasets, namely CNN transfer learning with discrete wavelet transform …
Unsupervised subtyping of cholangiocarcinoma using a deep clustering convolutional autoencoder
H Muhammad, CS Sigel, G Campanella… - … Image Computing and …, 2019 - Springer
Unlike common cancers, such as those of the prostate and breast, tumor grading in rare
cancers is difficult and largely undefined because of small sample sizes, the sheer volume of …
cancers is difficult and largely undefined because of small sample sizes, the sheer volume of …
Unsupervised feature learning with K-means and an ensemble of deep convolutional neural networks for medical image classification
Medical image analysis using supervised deep learning methods remains problematic
because of the reliance of deep learning methods on large amounts of labelled training …
because of the reliance of deep learning methods on large amounts of labelled training …
Patch-to-Sample Reasoning for Cervical Cancer Screening of Whole Slide Image
Deep learning has been instrumental in improving the accuracy of cervical cancer screening
using whole-slide images (WSIs) in recent years. Due to the complexity of the computer …
using whole-slide images (WSIs) in recent years. Due to the complexity of the computer …
Accelerating Spherical k-Means
E Schubert, A Lang, G Feher - International Conference on Similarity …, 2021 - Springer
Spherical k-means is a widely used clustering algorithm for sparse and high-dimensional
data such as document vectors. While several improvements and accelerations have been …
data such as document vectors. While several improvements and accelerations have been …
The seeding algorithm for spherical k-means clustering with penalties
S Ji, D Xu, L Guo, M Li, D Zhang - Journal of Combinatorial Optimization, 2022 - Springer
Spherical k-means clustering as a known NP-hard variant of the k-means problem has
broad applications in data mining. In contrast to k-means, it aims to partition a collection of …
broad applications in data mining. In contrast to k-means, it aims to partition a collection of …
Unsupervised segmentation of COVID-19 infected lung clinical CT volumes using image inpainting and representation learning
This paper newly proposes a segmentation method of infected area for COVID-19
(Coronavirus Disease 2019) infected lung clinical CT volumes. COVID-19 spread globally …
(Coronavirus Disease 2019) infected lung clinical CT volumes. COVID-19 spread globally …