The ACROBAT 2022 Challenge: Automatic Registration Of Breast Cancer Tissue

The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications. Advances in computing, deep learning, and availability of large WSI datasets have revolutionised WSI analysis. Therefore, the current state-of-the-art in WSI registration is unclear. To address this, we conducted the ACROBAT challenge, based on the largest WSI registration dataset to date, including 4,212 WSIs from 1,152 breast cancer patients. The challenge objective was to align WSIs of tissue that was stained with routine diagnostic immunohistochemistry to its H&E-stained counterpart. We compare the performance of eight WSI registration algorithms, including an investigation of the impact of different WSI properties and clinical covariates. We find that conceptually distinct WSI registration methods can lead to highly accurate registration performances and identify covariates that impact performances across methods. These results establish the current state-of-the-art in WSI registration and guide researchers in selecting and developing methods

Results from the Cuore Experiment

The Cryogenic Underground Observatory for Rare Events (CUORE) is the first bolometric experiment searching for neutrinoless double beta decay that has been able to reach the 1-ton scale. The detector consists of an array of 988 TeO2 crystals arranged in a cylindrical compact structure of 19 towers, each of them made of 52 crystals. The construction of the experiment was completed in August 2016 and the data taking started in spring 2017 after a period of commissioning and tests. In this work we present the neutrinoless double beta decay results of CUORE from examining a total TeO2 exposure of 86.3kg yr, characterized by an effective energy resolution of 7.7 keV FWHM and a background in the region of interest of 0.014 counts/ (keV kg yr). In this physics run, CUORE placed a lower limit on the decay half- life of neutrinoless double beta decay of 130Te > 1.3.1025 yr (90% C. L.). Moreover, an analysis of the background of the experiment is presented as well as the measurement of the 130Te 2vo3p decay with a resulting half- life of T2 2. [7.9 :- 0.1 (stat.) :- 0.2 (syst.)] x 10(20) yr which is the most precise measurement of the half- life and compatible with previous results

La Bibbia E L'Iliade

LA BIBBIA E L'ILIADE La Bibbia E L'Iliade ( - ) Einband ( - ) Titelseite ( - ) Widmung ( - ) Einleitung ( - ) I. (7) II. (21

Semi-supervised learning with a teacher-student paradigm for histopathology classification ::a resource to face data heterogeneity and lack of local annotations

Training classification models in the medical domain is often difficult due to data heterogeneity (related to acquisition procedures) and due to the difficulty of getting sufficient amounts of annotations from specialized experts. It is particularly true in digital pathology, where models do not generalize easily. This paper presents a novel approach for the generalization of models in conditions where heterogeneity is high and annotations are few. The approach relies on a semi-supervised teacher/student paradigm to different datasets and annotations. The paradigm combines a small amount of strongly-annotated data, with a large amount of unlabeled data, for training two Convolutional Neural Networks (CNN): the teacher and the student model. The teacher model is trained with strong labels and used to generate pseudo-labeled samples from the unlabeled data. The student model is trained combining the pseudo-labeled samples and a small amount of strongly-annotated data. The paradigm is evaluated on the student model performance of Gleason pattern and Gleason score classification in prostate cancer images and compared with a fully-supervised learning approach for training the student model. In order to evaluate the capability of the approach to generalize, the datasets used are highly heterogeneous in visual characteristics and are collected from two different medical institutions. The models, trained with the teacher/student paradigm, show an improvement in performance above the fully-supervised training. The models generalize better on both the datasets, despite the inter-datasets heterogeneity, alleviating the overfitting. The classification performance shows an improvement both in the classification of Gleason pattern at patch level ( from ) and at in Gleason score classification, evaluated at WSI-level from

Combining weakly and strongly supervised learning improves strong supervision in Gleason pattern classification

One challenge to train deep convolutional neural network (CNNs) models with whole slide images (WSIs) is providing the required large number of costly, manually annotated image regions. Strategies to alleviate the scarcity of annotated data include: using transfer learning, data augmentation and training the models with less expensive image-level annotations (weakly-supervised learning). However, it is not clear how to combine the use of transfer learning in a CNN model when different data sources are available for training or how to leverage from the combination of large amounts of weakly annotated images with a set of local region annotations. This paper aims to evaluate CNN training strategies based on transfer learning to leverage the combination of weak and strong annotations in heterogeneous data sources. The trade-off between classification performance and annotation effort is explored by evaluating a CNN that learns from strong labels (region annotations) and is later fine-tuned on a dataset with less expensive weak (image-level) labels

Semi-supervised training of deep convolutional neural networks with heterogeneous data and few local annotations ::an experiment on prostate histopathology image classification

Convolutional neural networks (CNNs) are state-of-the-art computer vision techniques for various tasks, particularly for image classification. However, there are domains where the training of classification models that generalize on several datasets is still an open challenge because of the highly heterogeneous data and the lack of large datasets with local annotations of the regions of interest, such as histopathology image analysis. Histopathology concerns the microscopic analysis of tissue specimens processed in glass slides to identify diseases such as cancer. Digital pathology concerns the acquisition, management and automatic analysis of digitized histopathology images that are large, having in the order of 100′0002 pixels per image. Digital histopathology images are highly heterogeneous due to the variability of the image acquisition procedures. Creating locally labeled regions (required for the training) is time-consuming and often expensive in the medical field, as physicians usually have to annotate the data. Despite the advances in deep learning, leveraging strongly and weakly annotated datasets to train classification models is still an unsolved problem, mainly when data are very heterogeneous. Large amounts of data are needed to create models that generalize well. This paper presents a novel approach to train CNNs that generalize to heterogeneous datasets originating from various sources and without local annotations. The data analysis pipeline targets Gleason grading on prostate images and includes two models in sequence, following a teacher/student training paradigm. The teacher model (a high-capacity neural network) automatically annotates a set of pseudo-labeled patches used to train the student model (a smaller network). The two models are trained with two different teacher/student approaches: semi-supervised learning and semi-weekly supervised learning. For each of the two approaches, three student training variants are presented. The baseline is provided by training the student model only with the strongly annotated data. Classification performance is evaluated on the student model at the patch level (using the local annotations of the Tissue Micro-Arrays Zurich dataset) and at the global level (using the TCGA-PRAD, The Cancer Genome Atlas-PRostate ADenocarcinoma, whole slide image Gleason score). The teacher/student paradigm allows the models to better generalize on both datasets, despite the inter-dataset heterogeneity and the small number of local annotations used. The classification performance is improved both at the patch-level (up to κ = 0.6127 ± 0.0133 from κ = 0.5667 ± 0.0285), at the TMA core-level (Gleason score) (up to κ = 0.7645 ± 0.0231 from κ = 0.7186 ± 0.0306) and at the WSI-level (Gleason score) (up to κ = 0.4529 ± 0.0512 from κ = 0.2293 ± 0.1350). The results show that with the teacher/student paradigm, it is possible to train models that generalize on datasets from entirely different sources, despite the inter-dataset heterogeneity and the lack of large datasets with local annotations