Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology

Stain variation is a phenomenon observed when distinct pathology laboratories stain tissue slides that exhibit similar but not identical color appearance. Due to this color shift between laboratories, convolutional neural networks (CNNs) trained with images from one lab often underperform on unseen images from the other lab. Several techniques have been proposed to reduce the generalization error, mainly grouped into two categories: stain color augmentation and stain color normalization. The former simulates a wide variety of realistic stain variations during training, producing stain-invariant CNNs. The latter aims to match training and test color distributions in order to reduce stain variation. For the first time, we compared some of these techniques and quantified their effect on CNN classification performance using a heterogeneous dataset of hematoxylin and eosin histopathology images from 4 organs and 9 pathology laboratories. Additionally, we propose a novel unsupervised method to perform stain color normalization using a neural network. Based on our experimental results, we provide practical guidelines on how to use stain color augmentation and stain color normalization in future computational pathology applications.Comment: Accepted in the Medical Image Analysis journa

Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images

Automated classification of histopathological whole-slide images (WSI) of breast tissue requires analysis at very high resolutions with a large contextual area. In this paper, we present context-aware stacked convolutional neural networks (CNN) for classification of breast WSIs into normal/benign, ductal carcinoma in situ (DCIS), and invasive ductal carcinoma (IDC). We first train a CNN using high pixel resolution patches to capture cellular level information. The feature responses generated by this model are then fed as input to a second CNN, stacked on top of the first. Training of this stacked architecture with large input patches enables learning of fine-grained (cellular) details and global interdependence of tissue structures. Our system is trained and evaluated on a dataset containing 221 WSIs of H&E stained breast tissue specimens. The system achieves an AUC of 0.962 for the binary classification of non-malignant and malignant slides and obtains a three class accuracy of 81.3% for classification of WSIs into normal/benign, DCIS, and IDC, demonstrating its potentials for routine diagnostics

From Project to Strategic Vision: Taking the Lead in Research Data Management Support at the University of Sydney Library

This paper explores three stories, each occurring a year apart, illustrating an evolution toward a strategic vision for Library leadership in supporting research data management at the University of Sydney. The three stories describe activities undertaken throughout the Seeding the Commons project and beyond, as the establishment of ongoing roles and responsibilities transition the Library from project partner to strategic leader in the delivery of research data management support. Each story exposes key ingredients that characterise research data management support: researcher engagement; partnerships; and the complementary roles of policy and practice

HookNet: multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images

We propose HookNet, a semantic segmentation model for histopathology whole-slide images, which combines context and details via multiple branches of encoder-decoder convolutional neural networks. Concentricpatches at multiple resolutions with different fields of view are used to feed different branches of HookNet, and intermediate representations are combined via a hooking mechanism. We describe a framework to design and train HookNet for achieving high-resolution semantic segmentation and introduce constraints to guarantee pixel-wise alignment in feature maps during hooking. We show the advantages of using HookNet in two histopathology image segmentation tasks where tissue type prediction accuracy strongly depends on contextual information, namely (1) multi-class tissue segmentation in breast cancer and, (2) segmentation of tertiary lymphoid structures and germinal centers in lung cancer. Weshow the superiority of HookNet when compared with single-resolution U-Net models working at different resolutions as well as with a recently published multi-resolution model for histopathology image segmentatio

Keith Stirling : An introduction to his life and examination of his music

This study introduces the life and examines the music of Australian jazz trumpeter Keith Stirling (1938-2003). The paper discusses the importance and position of Stirling in the jazz culture of Australian music, introducing key concepts that were influential not only to the development of Australian jazz but also in his life. Subsequently, a discussion of Stirling’s metaphoric tendencies provides an understanding of his philosophical perspectives toward improvisation as an art form. Thereafter, a discourse of the research methodology that was used and the resources that were collected throughout the study introduce a control group of transcriptions. These transcriptions provide an origin of phrases with which to discuss aspects of Stirling’s improvisational style. Instrumental approaches and harmonic concepts are then discussed and exemplified through the analysis of the transcribed phrases. Stirling’s instrumental techniques and harmonic concepts are examined by means of his own and student’s hand written notes and quotes from lesson recordings that took place in the early 1980s

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Investigation of unintentional indium incorporation into GaN barriers of InGaN/GaN quantum well structures

High resolution transmission electron microscopy has been employed to investigate the impact of the GaN bar-rier growth technique on the composition profile of InGaN quantum wells (QWs). We show that the profiles deviate from their nominal configuration due to the pres-ence of an indium tail at the upper interface of the QW. This indium tail, thought to be associated with a segrega-tion effect from the indium surfactant layer, has been shown to strongly depend on the growth method. The ef-fect of this tail has been investigated using a self-consistent Schrödinger-Poisson simulation. For the simu-lated conditions, a graded upper interface has been found to result in a decreased electron-hole wavefunction over-lap of up to 31 % compared to a QW with a rectangular profile, possibly leading to a decrease in radiative-recombination rate. Therefore in order to maximize the efficiency of a QW structure, it is important to grow the active region using a growth method which leads to QW interfaces which are as abrupt as possible. The results of this experiment find applications in every study where the emission properties of a device are correlated to a particular active region design.The authors acknowledge support from the EPSRC under EP/H0495331.This is the final version. It was first published by Wiley at http://onlinelibrary.wiley.com/doi/10.1002/pssb.201451543/abstrac