Deep Convolutional Neural Networks for Histological Image Analysis in Gastric Carcinoma Whole Slide Images

Introduction/ Background In this paper, histopathological whole slide images of gastric carcinoma are analyzed using deep learning methods. A convolutional neural network architecture is proposed for two classification applications in H&E stained tissue images, namely, cancer classification based on immunohistochemistry (IHC) into classes Her2/neu+ tumor, Her2/neu- tumor and non-tumor, and necrosis detection based on existence of necrosis into classes necrotic and non-necrotic. The studies in [1] and [2] explored computer-aided classification using graphbased methods and necrosis detection by textural approach respectively, which are extended using deep convolutional neural networks. Performance is quantitatively compared with established handcrafted image features, namely Haralick GLCM, Gabor filter-banks, LBP histograms, Gray histograms, RGB histograms and HSV histograms followed by classification by random forests, another well-known machine learning algorithm. Aims Convolutional neural networks (CNN) have recently gained tremendous attention in general image analysis [3-5]. There has also been an emergence of deep learning in digital histopathology for diverse classification and detection problems [6-8]. The prime motivation behind this work is that no previous study has explored deep learning for the specified goals in gastric cancer WSI. Automated cancer classification can assist pathologists in computer-aided diagnosis in H&E stained WSI without the requirement of IHC staining, thereby reducing preparation and inspection times, and decreasing inter- and intra-observer variability. Necrosis detection can play an important role in prognosis, as larger necrotic areas indicate a smaller chance of survival and vice-versa. Moreover, most deep learning studies have used smaller image sizes mainly due to memory restrictions of GPU, however, we consider larger regions in order to preserve context i.e. neighborhood information and tissue architecture at higher magnification. Further, this method is independent of nuclei segmentation, hence its performance is not limited by segmentation performance as in [1] (evaluation details in [9]). Methods Firstly, standard data augmentation techniques are applied on the available gastric cancer WSI dataset and thousands of images of size 512x512 are generated. Different CNN architectures are empirically studied to observe the behavior of variation in model characteristics (network depth, layer properties, training parameters, etc.) by training them from scratch on a representative subset of whole data for cancer classification. One of these is the Imagenet model [4], however it doesn’t perform desirably on the representative dataset. The self-designed CNN architecture with best classification rates is selected. Later, the proposed CNN is also applied for necrosis detection. Performance is compared with state of the art methods using handcrafted features and random forests. For evaluation, randomized three-fold stratified shuffle split and leave-one-patient-out cross validations are used. Results Conclusion: A self-designed CNN architecture is proposed for image analysis (cancer classification based on IHC and necrosis detection) in H&E stained WSI of gastric cancer. Quantitative evaluation shows that deep learning methods mostly compare favorably to state of the art methods, especially for necrosis detection. In future the aim is to expand the current WSI dataset and to improve the CNN architecture for optimal performance

Towards global volcano monitoring using multisensor sentinel missions and artificial intelligence: The MOUNTS monitoring system

Most of the world’s 1500 active volcanoes are not instrumentally monitored, resulting in deadly eruptions which can occur without observation of precursory activity. The new Sentinel missions are now providing freely available imagery with unprecedented spatial and temporal resolutions, with payloads allowing for a comprehensive monitoring of volcanic hazards. We here present the volcano monitoring platform MOUNTS (Monitoring Unrest from Space), which aims for global monitoring, using multisensor satellite-based imagery (Sentinel-1 Synthetic Aperture Radar SAR, Sentinel-2 Short-Wave InfraRed SWIR, Sentinel-5P TROPOMI), ground-based seismic data (GEOFON and USGS global earthquake catalogues), and artificial intelligence (AI) to assist monitoring tasks. It provides near-real-time access to surface deformation, heat anomalies, SO2 gas emissions, and local seismicity at a number of volcanoes around the globe, providing support to both scientific and operational communities for volcanic risk assessment. Results are visualized on an open-access website where both geocoded images and time series of relevant parameters are provided, allowing for a comprehensive understanding of the temporal evolution of volcanic activity and eruptive products. We further demonstrate that AI can play a key role in such monitoring frameworks. Here we design and train a Convolutional Neural Network (CNN) on synthetically generated interferograms, to operationally detect strong deformation (e.g., related to dyke intrusions), in the real interferograms produced by MOUNTS. The utility of this interdisciplinary approach is illustrated through a number of recent eruptions (Erta Ale 2017, Fuego 2018, Kilauea 2018, Anak Krakatau 2018, Ambrym 2018, and Piton de la Fournaise 2018–2019). We show how exploiting multiple sensors allows for assessment of a variety of volcanic processes in various climatic settings, ranging from subsurface magma intrusion, to surface eruptive deposit emplacement, pre/syn-eruptive morphological changes, and gas propagation into the atmosphere. The data processed by MOUNTS is providing insights into eruptive precursors and eruptive dynamics of these volcanoes, and is sharpening our understanding of how the integration of multiparametric datasets can help better monitor volcanic hazards

Reconstruction of body cavity volume in terrestrial tetrapods

tracts required for the digestion of plant fiber, this concept has not been addressed quantitatively. We estimated the volume of the torso in 126 terrestrial tetrapods (synapsids including basal synapsids and mammals, and diapsids including birds, non-avian dinosaurs and reptiles) classified as either herbivore or carnivore in digital models of mounted skeletons, using the convex hull method. The difference in relative torso volume between diet types was significant in mammals, where relative torso volumes of herbivores were about twice as large as that of carnivores, supporting the general hypothesis. However, this effect was not evident in diapsids. This may either reflect the difficulty to reliably reconstruct mounted skeletons in non-avian dinosaurs, or a fundamental difference in the bauplan of different groups of tetrapods, for example due to differences in respiratory anatomy. Evidently, the condition in mammals should not be automatically assumed in other, including more basal, tetrapod lineages. In both synapsids and diapsids, large animals showed a high degree of divergence with respect to the proportion of their convex hull directly supported by bone, with animals like elephants or Triceratops having a low proportion, and animals such as rhinoceros having a high proportion of bony support. The relevance of this difference remains to be further investigated

A new body mass estimation of <i>Brachiosaurus brancai</i> Janensch, 1914 mounted and exhibited at the Museum of Natural History (Berlin, Germany)

Body mass and surface areas are important in several aspects for an organism living today. Therefore, mass and surface determinations for extinct dinosaurs could be important for paleo-biological aspects as well. Based on photogrammetrical measurement the body mass and body surface area of the Late Jurassic Brachiosaurus brancai Janensch, 1914 from Tendaguru (East Africa), a skeleton mounted and exhibited at the Museum of Natural History in Berlin (Germany), has been re-evaluated. We determined for a slim type of 3D reconstruction of Brachiosaurus brancai a total volume of 47.9 m3 which represents, assuming a mean tissue density of 0.8 kg per 1,000 cm3, a total body mass of 38,000 kg. The volume distributions from the head to the tail were as follows: 0.2 m3 for the head, neck 7.3 m3, fore limbs 2.9 m3, hind limbs 2.6 m3, thoracic-abdominal cavity 32.4 m3, tail 2.2 m3. The total body surface area was calculated to be 119.1 m2, specifically 1.5 m2 for the head, 26 m2 neck, fore limbs 18.8 m2, hind limbs 16.4 m2, 44.2 m2 thoracic-abdominal cavity, and finally the tail 12.2 m2. Finally, allometric equations were used to estimate presumable organ sizes of this extinct dinosaur and to test whether their dimensions really fit into the thoracic and abdominal cavity of Brachiosaurus brancai if a slim body shape of this sauropod is assumed. doi:10.1002/mmng.200700011</a

The complex X-ray spectrum of NGC 4507

XMM-Newton and Chandra/HETG spectra of the Compton-thin (NH 4x10^{23} cm^{-2}) Seyfert 2 galaxy, NGC 4507, are analyzed and discussed. The main results are: a) the soft X-ray emission is rich in emission lines; an (at least) two--zone photoionization region is required to explain the large range of ionization states. b) The 6.4 keV iron line is likely emitted from Compton-thick matter, implying the presence of two circumnuclear cold regions, one Compton-thick (the emitter), one Compton-thin (the cold absorber). c) Evidence of an Fe xxv absorption line is found in the Chandra/HETG spectrum. The column density of the ionized absorber is estimated to be a few x10^{22} cm^{-2}.Comment: accepted for publication in A&

FASTER TREES: STRATEGIES FOR ACCELERATED TRAINING AND PREDICTION OF RANDOM FORESTS FOR CLASSIFICATION OF POLSAR IMAGES

Random Forests have continuously proven to be one of the most accurate, robust, as well as efficient methods for the supervised classification of images in general and polarimetric synthetic aperture radar data in particular. While the majority of previous work focus on improving classification accuracy, we aim for accelerating the training of the classifier as well as its usage during prediction while maintaining its accuracy. Unlike other approaches we mainly consider algorithmic changes to stay as much as possible independent of platform and programming language. The final model achieves an approximately 60 times faster training and a 500 times faster prediction, while the accuracy is only marginally decreased by roughly 1&thinsp;%

TASK-DEPENDENT BAND-SELECTION OF HYPERSPECTRAL IMAGES BY PROJECTION-BASED RANDOM FORESTS

The automatic classification of land cover types from hyperspectral images is a challenging problem due to (among others) the large amount of spectral bands and their high spatial and spectral correlation. The extraction of meaningful features, that enables a subsequent classifier to distinguish between different land cover classes, is often limited to a subset of all available data dimensions which is found by band selection techniques or other methods of dimensionality reduction. This work applies Projection-Based Random Forests to hyperspectral images, which not only overcome the need of an explicit feature extraction, but also provide mechanisms to automatically select spectral bands that contain original (i.e. non-redundant) as well as highly meaningful information for the given classification task. The proposed method is applied to four challenging hyperspectral datasets and it is shown that the effective number of spectral bands can be considerably limited without loosing too much of classification performance, e.g. a loss of 1&thinsp;% accuracy if roughly 13&thinsp;% of all available bands are used

COLOR TEXTONS FOR BUILDING DETECTION

Textons are known to be powerful operators in capturing textural properties of image regions. This paper proposes a new method to consistently combine structural cues as well as color information in an unified framework of color textons. They are used as features to detect buildings from optical imagery. Despite the simple classification algorithm, presented results are promising and show the usefulness of the proposed feature operator in remote sensing applications

COMPLEMENTARITY OF SAR POLARIMETRY AND TOMOGRAPHY FOR BUILDING DETECTION AND CHARACTERIZATION

In this paper we propose to study the potential of jointly using polarimetric and tomographic SAR data to recognize and localize buildings in complex scenarios. We present extraction methods for both polarimetric and tomographic features. One the one hand, we propose to use the polarimetric bilateral filter that has proved to be a powerful tool to retrieve the polarimetric covariance matrix while reducing speckle and preserving edges. Thus, polarimetric decompositions can be used for physical interpretation. On the other hand, the TomoSNI and TomoSeed algorithms allow to respectively extract interest points and segment geometric primitives in 3D point clouds obtained with tomographic focusing methods. We show how the output of such algorithms could be combined in order to allow the extraction of buildings. We also analyze different issues related to complex scenarios that may impede a correct detection and discuss some possible solutions