Deep learning for whole slide image analysis : an overview

The widespread adoption of whole slide imaging has increased the demand for effective and efficient gigapixel image analysis. Deep learning is at the forefront of computer vision, showcasing significant improvements over previous methodologies on visual understanding. However, whole slide images have billions of pixels and suffer from high morphological heterogeneity as well as from different types of artifacts. Collectively, these impede the conventional use of deep learning. For the clinical translation of deep learning solutions to become a reality, these challenges need to be addressed. In this paper, we review work on the interdisciplinary attempt of training deep neural networks using whole slide images, and highlight the different ideas underlying these methodologies.Publisher PDFPeer reviewe

Deep learning for whole slide image analysis:an overview

The widespread adoption of whole slide imaging has increased the demand for effective and efficient gigapixel image analysis. Deep learning is at the forefront of computer vision, showcasing significant improvements over previous methodologies on visual understanding. However, whole slide images have billions of pixels and suffer from high morphological heterogeneity as well as from different types of artifacts. Collectively, these impede the conventional use of deep learning. For the clinical translation of deep learning solutions to become a reality, these challenges need to be addressed. In this paper, we review work on the interdisciplinary attempt of training deep neural networks using whole slide images, and highlight the different ideas underlying these methodologies

Corrigendum:Deep Learning for Whole Slide Image Analysis: An Overview (Front. Med. (2019), 6, (264), 10.3389/fmed.2019.00264)

Equal contribution was incorrectly attributed to the authors. Instead, the statement “These authors have contributed equally to this work” should be removed.In the published article, Peter D Caie was incorrectly listed as the corresponding author. Instead, Neofytos Dimitriou should be the corresponding author. The corresponding author’s email address should read [email protected]. The authors apologize for these errors and state that these do not change the scientific conclusions of the article in any way. The original article has been updated.</p

Corrigendum:Deep Learning for Whole Slide Image Analysis: An Overview (Front. Med. (2019), 6, (264), 10.3389/fmed.2019.00264)

Equal contribution was incorrectly attributed to the authors. Instead, the statement “These authors have contributed equally to this work” should be removed.In the published article, Peter D Caie was incorrectly listed as the corresponding author. Instead, Neofytos Dimitriou should be the corresponding author. The corresponding author’s email address should read [email protected]. The authors apologize for these errors and state that these do not change the scientific conclusions of the article in any way. The original article has been updated.</p

Data-mining twitter and the autism spectrum disorder: a pilot study

The autism spectrum disorder (ASD) is increasingly being recognized as a major public health issue which affects approximately 0.5-0.6% of the population. Promoting the general awareness of the disorder, increasing the engagement with the affected individuals and their carers, and understanding the success of penetration of the current clinical recommendations in the target communities, is crucial in driving research as well as policy. The aim of the present work is to investigate if Twitter, as a highly popular platform for information exchange, can be used as a data-mining source which could aid in the aforementioned challenges. Specifically, using a large data set of harvested tweets, we present a series of experiments which examine a range of linguistic and semantic aspects of messages posted by individuals interested in ASD. Our findings, the first of their nature in the published scientific literature, strongly motivate additional research on this topic and present a methodological basis for further work

Colorectal cancer outcome prediction from H&amp;E whole slide images using machine learning and automatically inferred phenotype profiles

Digital pathology (DP) is a new research area which falls under the broad umbrella of health informatics. Owing to its potential for major public health impact, in recent years DP has been attracting much research attention. Nevertheless, a wide breadth of significant conceptual and technical challenges remain, few of them greater than those encountered in the field of oncology. The automatic analysis of digital pathology slides of cancerous tissues is particularly problematic due to the inherent heterogeneity of the disease, extremely large images, amongst numerous others. In this paper we introduce a novel machine learning based framework for the prediction of colorectal cancer outcome from whole digitized haematoxylin &amp; eosin (H&amp;E) stained histopathology slides. Using a real-world data set we demonstrate the effectiveness of the method and present a detailed analysis of its different elements which corroborate its ability to extract and learn salient, discriminative, and clinically meaningful content.</p

Mechanochemical synthesis of CaTiO3 from CaCO3 - TiO2 mixture

The synthesis of calcium titanate, CaTiO3, was performed by mechanical activation and thermal treatment. Milling for up to 360 minutes in a planetary ball mill mechanically activated an equimolar mixture of CaCO 3 and TiO2 powders. A small amount of mechanically activated mixtures was pressed into briquettes and calcined at 850°C for two hours. The effect of mechanical activation on the solid-state reaction was studied using X-ray powder diffraction and differential thermal analysis. The change of morphology and size of powder particles due to milling, were determined by SEM, while BET analysis was used to determine the specific surface area of the powder. The sintering process was followed by a dilatometer during thermal treatment up to 1300°C. The main conclusion of the analysis of conducted investigations is that CaTiO3 ceramics can be obtained from an activated mixture at a much lower temperature than reported in the literature owing to acceleration of the chemical reaction and sintering

Self-supervised learning of audio-visual objects from video

Our objective is to transform a video into a set of discrete audio-visual objects using self-supervised learning. To this end, we introduce a model that uses attention to localize and group sound sources, and optical flow to aggregate information over time. We demonstrate the effectiveness of the audio-visual object embeddings that our model learns by using them for four downstream speech-oriented tasks: (a) multi-speaker sound source separation, (b) localizing and tracking speakers, (c) correcting misaligned audio-visual data, and (d) active speaker detection. Using our representation, these tasks can be solved entirely by training on unlabeled video, without the aid of object detectors. We also demonstrate the generality of our method by applying it to non-human speakers, including cartoons and puppets. Our model significantly outperforms other self-supervised approaches, and obtains performance competitive with methods that use supervised face detection

Descriptor Learning Using Convex Optimisation

The objective of this work is to learn descriptors suitable for the sparse feature detectors used in viewpoint invariant matching. We make a number of novel contributions towards this goal: first, it is shown that learning the pooling regions for the descriptor can be formulated as a convex optimisation problem selecting the regions using sparsity; second, it is shown that dimensionality reduction can also be formulated as a convex optimisation problem, using the nuclear norm to reduce dimensionality. Both of these problems use large margin discriminative learning methods. The third contribution is a new method of obtaining the positive and negative training data in a weakly supervised manner. And, finally, we employ a state-of-the-art stochastic optimizer that is efficient and well matched to the non-smooth cost functions proposed here. It is demonstrated that the new learning methods improve over the state of the art in descriptor learning for large scale matching, Brown et al. [2], and large scale object retrieval, Philbin et al. [10]. © 2012 Springer-Verlag