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A Generalized Hierarchical Histological Tissue Type-Annotated Database for Deep Learning

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A Generalized Hierarchical Histological Tissue Type-Annotated Database for Deep Learning Atlas of Digital Pathology: A Generalized Hierarchical Histological Tissue Type-Annotated Database for Deep Learning Mahdi S. Hosseini1,4, Lyndon Chan 1, Gabriel Tse 1, Michael Tang 1, Jun Deng 1, Sajad Norouzi 1 Corwyn Rowsell 2,3, Konstantinos N. Plataniotis 1, and Savvas Damaskinos 4 1The Edward S. Rogers Sr. Department of Electrical & Computer Engineering, University of Toronto 2Division of Pathology, St. Michaels Hospital, Toronto, ON, M4N 1X3, Canada 3Department of Laboratory Medicine and Pathobiology, University of Toronto 4Huron Digital Pathology, St. Jacobs, ON, N0B 2N0, Canada [email protected] Abstract dogs and cats) [9, 32, 16, 33, 12, 38], computational pathol- ogy promises to improve the diagnostic accuracy and pro- In recent years, computer vision techniques have made ductivity of pathologists, who face an increasingly heavy large advances in image recognition and been applied workload [27, 41, 21, 15]. In fact, the use of computer vi- to aid radiological diagnosis. Computational pathology sion algorithms in computer-aided diagnosis (CADx) has aims to develop similar tools for aiding pathologists in di- already become widely accepted in the sister field of radi- agnosing digitized histopathological slides, which would ology [29, 40, 5]. But as there exist serious shortcomings improve diagnostic accuracy and productivity amidst in- in publicly-available digital pathology databases, and most creasing workloads. However, there is a lack of publicly- state-of-the-art computer vision systems are trained by su- available databases of (1) localized patch-level images an- pervised learning, this constrains the development of useful notated with (2) a large range of Histological Tissue Type and generalizable computational pathology tools. (HTT). As a result, computational pathology research is Most databases are either annotated with textual descrip- constrained to diagnosing specific diseases or classifying tions or annotated at the slide level with minimal local- tissues from specific organs, and cannot be readily general- ized information provided by drawn outlines [21]. This ized to handle unexpected diseases and organs. is problematic because, unlike general image classification, In this paper, we propose a new digital pathology digital pathology slide images are very large compared to database, the “Atlas of Digital Pathology” (or ADP), which the features of interest, where local patch-level (or even comprises of 17,668 patch images extracted from 100 slides pixel-level) information is essential. Furthermore, existing annotated with up to 57 hierarchical HTTs. Our data is databases are specially annotated with an incomplete set of generalized to different tissue types across different organs possible tissue conditions (i.e. healthy, disease #1, disease and aims to provide training data for supervised multi-label #2, ..., disease #N) and hence cannot be generalized to clas- learning of patch-level HTT in a digitized whole slide im- sify unexpected diseases or unseen organs. Since only a age. We demonstrate the quality of our image labels through limited number of possible tissue types are observable, it pathologist consultation and by training three state-of-the- makes more sense to annotate tissue types. Some databases art neural networks on tissue type classification. Quantita- do just that, but use only a few tissue types, which again tive results support the visually consistency of our data and limits their application. This lack of (1) localized patch- we demonstrate a tissue type-based visual attention aid as level images annotated with (2) a large range of Histological a sample tool that could be developed from our database. Tissue Type (HTT) prevents computational pathology from properly developing generalized clinically useful tools. 1 Introduction Inspired by this shortcoming in publicly available digi- The field of computational pathology aims to develop tal pathology databases, we introduce the Atlas of Digital computer algorithms to assist the diagnosis of disease from Pathology (ADP or Atlas, for short), a database of digi- digital pathology images by pathologists [21]. Leveraging tal pathology image patches originating from a variety of the impressive advances of computer vision in solving dif- organs acquired by a whole slide imaging (WSI) scanner, ficult general image recognition problems (e.g. recognizing and each annotated with multiple HTTs (both morpholog- 432111747 ical and functional) organized in a hierarchical taxonomy. Human Protein Atlas [31], The Cancer Genome Atlas [42], To the best of our knowledge, there are no other similar and the Stanford Tissue Microarray Database [26]. These databases of digital pathology originating from different or- databases are very large and cataloged with structured slide- gans and annotated at the patch level with multiple, hierar- level mixed-format annotations (e.g. patient data, survival chical HTTs, and this makes our database singularly well- outcomes, pathologist diagnosis). Chang et al. train three- suited for training useful computational pathology tools. class tissue classifiers with TCGA in [6] and Sirinukunwat- Our database annotations are validated by an experi- tana et al. train four-class nuclei classifiers with STMD in enced pathologist which uniquely exploits a priori knowl- [35]. The second type is released by medical teaching de- edge of the hierarchical relations between HTTs. As it is partments for educating their students in histology, contain labeled with tissue type at the patch level, it can be used fewer examples, and are annotated with unstructured slide- to develop a computational pathology system for assisting level textual descriptions (e.g. labels pointing at diseased pathologists in their diagnostic evaluations of whole slide regions) - these are very difficult to apply to computational images with patch-level resolution. Furthermore, we adopt pathology. The third type is released by academic research three existing CNN architectures - VGG16 [33], ResNet18 departments and teaching hospitals for evaluating compu- [12], and Inception-V3 [38] - and validate the quality of tational algorithms applied to a chosen clinically-relevant our labeling by training them for tissue type classification problem, known as Grand Challenges, and include the MIC- using multi-label hierarchical classification loss. The ex- CAI Nuclei Segmentation [18], MICCAI Gland Segmenta- cellent predictive performance of these networks not only tion [34], and CAMELYON [20] contests. These databases further validates the labeling quality, but also suggests the tend to be moderately large in size, focused on particular possibility of applying the data to develop computational tissues or conditions, and cataloged with patch-level cate- pathology tools for solving more difficult related problems. gorical or positional annotations relevant to the problem at An obvious immediate application of the Atlas of Dig- hand (e.g. cancerous or non-cancerous, locations of mitotic ital Pathology would be the development of a multi-label figures). These databases are commonly used for computa- supervised learning algorithm for predicting patch-level tis- tional pathology: Chen et al. won MICCAI Gland Segmen- sue type, which could be used to localize tissue types in a tation 2015 with their DCAN [7], while Lee and Baeng won whole slide image as an automated attention aid for pathol- CAMELYON17 with their multi-stage framework [19]. ogists. For instance, such a tool could highlight glandu- 1.2 What is Missing? lar tissue regions for cancer diagnosis or show regions of As stated above, the existing databases are focused on tumor-infiltrating lymphocytes for disease prognosis, thus slides exhibiting particular organs (e.g. breast tissue) either helping pathologists streamline their visual search proce- annotated at the slide level with particular diagnostic condi- dure. Our Atlas data could be feasibly extended to develop: tions (e.g. cancer grades) or at the local patch level with (a) pixel-level interpretation of WSI via weakly-supervised particular tissue structures (e.g. glands) to the exclusion semantic segmentation to obtain instance-level outlines of of other conditions and structures. Therefore, the current individual tissue structures which can assist pathologists in compilation format of annotated database limits the proper visually searching for relevant tissues, which is known to be means of developing tissue recognition tasks for computa- time consuming and inconsistent [17, 28]; (b) tissue mor- tion pathology in the first place. In fact, a meaningful recog- phometric analysis to automatically measure the shapes of nition tool should be capable of identifying a wide range of tissue structure, that is needed for diagnosing certain dis- tissue spectrum, so one can narrow down to certain tissue(s) eases [11]; (c) abnormality detection that would associate for diagnostic analysis. Our proposed database, though, in- disease prognosis with the shape and arrangement of tissue cludes histological tissue slides from different organs and structures [4]; and (d) image retrieval based on HTT en- annotated at the patch level with different tissue structures. coding which would enable more semantically-relevant ap- proaches than existing unsupervised Content-Based Image 2 Construction of Atlas Database Retrieval (CBIR) approaches [37, 24, 23, 44, 14, 25, 45, 46]. This section explains the construction of Atlas database 1.1 Related Work from diverse HTTs originating from various
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