Histopathology tissue analysis is considered the gold standard in cancer diagnosis and prognosis. Whole-slide imaging (WSI), i.e., the scanning and digitization of entire histology slides, are now being adopted across the world in pathology labs. Trained histopathologists can provide an accurate diagnosis of biopsy specimens based on WSI data. Given the dimensionality of WSIs and the increase in the number of potential cancer cases, analyzing these images is a time-consuming process. Automated segmentation of tumorous tissue helps in elevating the precision, speed, and reproducibility of research. In the recent past, deep learning-based techniques have provided state-of-the-art results in a wide variety of image analysis tasks, including the analysis of digitized slides. However, deep learning-based solutions pose many technical challenges, including the large size of WSI data, heterogeneity in images, and complexity of features. In this study, we propose a generalized deep learning-based framework for histopathology tissue analysis to address these challenges. Our framework is, in essence, a sequence of individual techniques in the preprocessing-training-inference pipeline which, in conjunction, improve the efficiency and the generalizability of the analysis. The combination of techniques we have introduced includes an ensemble segmentation model, division of the WSI into smaller overlapping patches while addressing class imbalances, efficient techniques for inference, and an efficient, patch-based uncertainty estimation framework. Our ensemble consists of DenseNet-121, Inception-ResNet-V2, and DeeplabV3Plus, where all the networks were trained end to end for every task. We demonstrate the efficacy and improved generalizability of our framework by evaluating it on a variety of histopathology tasks including breast cancer metastases (CAMELYON), colon cancer (DigestPath), and liver cancer (PAIP). Our proposed framework has state-of-the-art performance across all these tasks and is ranked within the top 5 currently for the challenges based on these datasets. The entire framework along with the trained models and the related documentation are made freely available at GitHub and PyPi. Our framework is expected to aid histopathologists in accurate and efficient initial diagnosis. Moreover, the estimated uncertainty maps will help clinicians to make informed decisions and further treatment planning or analysis.

/pdf/a-generalized-deep-learning-framework-for-whole-slide-image-17baof1vhf.pdf

A generalized deep learning framework for whole-slide image segmentation and analysis.

Weissella strains have been the subject of much research over the last 5 years because of the genus' technological and probiotic potential. Certain strains have attracted the attention of the pharmaceutical, medical, and food industries because of their ability to produce antimicrobial exopolysaccharides (EPSs). Moreover, Weissella strains are able to keep foodborne pathogens in check because of the bacteriocins, hydrogen peroxide, and organic acids they can produce; all listed have recognized pathogen inhibitory activities. The Weissella genus has also shown potential for treating atopic dermatitis and certain cancers. W. cibaria, W. confusa, and W. paramesenteroides are particularly of note because of their probiotic potential (fermentation of prebiotic fibers) and their ability to survive in the gastrointestinal tract. It is important to note that most of the Weissella strains with these health-promoting properties have been shown to be save safe, due to the absence or the low occurrence of virulence or antibiotic-resistant genes. A large number of scientific studies continue to report on and to support the use of Weissella strains in the food and pharmaceutical industries. This review provides an overview of these studies and draws conclusions for future uses of this rich and previously unexplored genus.

Weissella: An Emerging Bacterium with Promising Health Benefits.

Digital analysis of pathology whole-slide images is fast becoming a game changer in cancer diagnosis and treatment. Specifically, deep learning methods have shown great potential to support pathology analysis, with recent studies identifying molecular traits that were not previously recognized in pathology H&E whole-slide images. Simultaneous to these developments, it is becoming increasingly evident that tumor heterogeneity is an important determinant of cancer prognosis and susceptibility to treatment, and should therefore play a role in the evolving practices of matching treatment protocols to patients. State of the art diagnostic procedures, however, do not provide automated methods for characterizing and/or quantifying tumor heterogeneity, certainly not in a spatial context. Further, existing methods for analyzing pathology whole-slide images from bulk measurements require many training samples and complex pipelines. Our work addresses these two challenges. First, we train deep learning models to spatially resolve bulk mRNA and miRNA expression levels on pathology whole-slide images (WSIs). Our models reach up to 0.95 AUC on held-out test sets from two cancer cohorts using a simple training pipeline and a small number of training samples. Using the inferred gene expression levels, we further develop a method to spatially characterize tumor heterogeneity. Specifically, we produce tumor molecular cartographies and heterogeneity maps of WSIs and formulate a heterogeneity index (HTI) that quantifies the level of heterogeneity within these maps. Applying our methods to breast and lung cancer slides, we show a significant statistical link between heterogeneity and survival. Our methods potentially open a new and accessible approach to investigating tumor heterogeneity and other spatial molecular properties and their link to clinical characteristics, including treatment susceptibility and survival.

/pdf/spatial-transcriptomics-inferred-from-pathology-whole-slide-4kl5d4n14g.pdf

Spatial transcriptomics inferred from pathology whole-slide images links tumor heterogeneity to survival in breast and lung cancer

Deep learning trained on hematoxylin and eosin tumor region of Interest predicts HER2 status and trastuzumab treatment response in HER2+ breast cancer

The mitotic activity index is a key prognostic measure in tumour grading. Microscopy based detection of mitotic nuclei is a significant overhead and necessitates automation. This work proposes deep CNN based multi-phase mitosis detection framework "MP-MitDet" for mitotic nuclei identification in breast cancer histopathological images. The workflow constitutes: (1) label-refiner, (2) tissue-level mitotic region selection, (3) blob analysis, and (4) cell-level refinement. We developed an automatic label-refiner to represent weak labels with semi-sematic information for training of deep CNNs. A deep instance-based detection and segmentation model is used to explore probable mitotic regions on tissue patches. More probable regions are screened based on blob area and then analysed at cell-level by developing a custom CNN classifier "MitosRes-CNN" to filter false mitoses. The performance of the proposed "MitosRes-CNN" is compared with the state-of-the-art CNNs that are adapted to cell-level discrimination through cross-domain transfer learning and by adding task-specific layers. The performance of the proposed framework shows good discrimination ability in terms of F-score (0.75), recall (0.76), precision (0.71) and area under the precision-recall curve (0.78) on challenging TUPAC16 dataset. Promising results suggest good generalization of the proposed framework that can learn characteristic features from heterogenous mitotic nuclei.

/pdf/a-multi-phase-deep-cnn-based-mitosis-detection-framework-for-1eez1w919j.pdf

A multi-phase deep CNN based mitosis detection framework for breast cancer histopathological images.

We report on first experimental tests of a neutron magnetic spin resonator at a very cold neutron beam port of the high flux reactor at the ILL Grenoble. When placed between two supermirror neutron polarizers and operated in a pulsed traveling-wave mode it allows to decouple its time- and wavelength-resolution and can therefore be used simultaneously as electronically tunable monochromator and fast beam chopper. As a first ‘real’ scientific application we intend its implementation in the PERC (p roton and e lectron r adiation c hannel) project related to high-precision experiments in neutron beta decay.

https://repository.kulib.kyoto-u.ac.jp/dspace/bitstream/2433/261250/1/s41598-020-62612-9.pdf

MONOPOL - A traveling-wave magnetic neutron spin resonator for tailoring polarized neutron beams.

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MONOPOL - A traveling-wave magnetic neutron spin resonator for tailoring polarized neutron beams.