Atlas of Digital Pathology: A Generalized Hierarchical Histological Tissue Type-Annotated Database for Deep Learning

Hosseini, Mahdi S.; Chan, Lyndon; Tse, Gabriel; Tang, Michael Chi Seng; Deng, Jun; Norouzi, Sajad; Rowsell, Corwyn; Plataniotis, Konstantinos N.; Damaskinos, Savvas

doi:10.1109/cvpr.2019.01202

Cited by 54 publications

(20 citation statements)

References 38 publications

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“…Another course for future work would be applying our framework to other datasets. Concurrent with our work, Hosseini et al 26 published an annotated pathological image database with hierarchical ordering and it would be interesting to see if the cohorts' properties remain unchanged when our framework is applied to their dataset. Recall that similarity among cancer tissues' features affects the cancer detection models more than normal organ features: the grouped cohort models' performances showed a higher correlation with the positive discrimination model's metric than the negative model's.…”

Section: Discussionmentioning

confidence: 80%

Aggregation of cohorts for histopathological diagnosis with deep morphological analysis

Park¹,

Chung²,

Kong³

et al. 2021

Sci Rep

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There have been substantial efforts in using deep learning (DL) to diagnose cancer from digital images of pathology slides. Existing algorithms typically operate by training deep neural networks either specialized in specific cohorts or an aggregate of all cohorts when there are only a few images available for the target cohort. A trade-off between decreasing the number of models and their cancer detection performance was evident in our experiments with The Cancer Genomic Atlas dataset, with the former approach achieving higher performance at the cost of having to acquire large datasets from the cohort of interest. Constructing annotated datasets for individual cohorts is extremely time-consuming, with the acquisition cost of such datasets growing linearly with the number of cohorts. Another issue associated with developing cohort-specific models is the difficulty of maintenance: all cohort-specific models may need to be adjusted when a new DL algorithm is to be used, where training even a single model may require a non-negligible amount of computation, or when more data is added to some cohorts. In resolving the sub-optimal behavior of a universal cancer detection model trained on an aggregate of cohorts, we investigated how cohorts can be grouped to augment a dataset without increasing the number of models linearly with the number of cohorts. This study introduces several metrics which measure the morphological similarities between cohort pairs and demonstrates how the metrics can be used to control the trade-off between performance and the number of models.

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Section: Discussionmentioning

confidence: 80%

Aggregation of cohorts for histopathological diagnosis with deep morphological analysis

Park¹,

Chung²,

Kong³

et al. 2021

Sci Rep

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“…As a comparison, broadly used datasets like the ImageNet 2012 classification dataset cover more than 1000 classes in more than 1 million images [33]. Furthermore, even if certain published datasets contain histological images (for instance, the Atlas of Digital Pathology dataset with a significantly larger image cluster-17,668 images grouped into 42 classes), the image content and the task definition (recognition of the histological tissue type) substantially differ from our model [34]. We focused on highly specific glomerular architecture and set the task to recognize proportionally discrete pathological changes.…”

Section: Discussionmentioning

confidence: 98%

Assessment of glomerular morphological patterns by deep learning algorithms

et al. 2022

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Background Compilation of different morphological lesion signatures is characteristic of renal pathology. Previous studies have documented the potential value of artificial intelligence (AI) in recognizing relatively clear-cut glomerular structures and patterns, such as segmental or global sclerosis or mesangial hypercellularity. This study aimed to test the capacity of deep learning algorithms to recognize complex glomerular structural changes that reflect common diagnostic dilemmas in nephropathology. Methods For this purpose, we defined nine classes of glomerular morphological patterns and trained twelve convolutional neuronal network (CNN) models on these. The two-step training process was done on a first dataset defined by an expert nephropathologist (12,253 images) and a second consensus dataset (11,142 images) defined by three experts in the field. Results The efficacy of CNN training was evaluated using another set with 180 consensus images, showing convincingly good classification results (kappa-values 0.838–0.938). Furthermore, we elucidated the image areas decisive for CNN-based decision making by class activation maps. Finally, we demonstrated that the algorithm could decipher glomerular disease patterns coinciding in a single glomerulus (e.g. necrosis along with mesangial and endocapillary hypercellularity). Conclusions In summary, our model, focusing on glomerular lesions detectable by conventional microscopy, is the first sui generis to deploy deep learning as a reliable and promising tool in recognition of even discrete and/or overlapping morphological changes. Our results provide a stimulus for ongoing projects that integrate further input levels next to morphology (such as immunohistochemistry, electron microscopy, and clinical information) to develop a novel tool applicable for routine diagnostic nephropathology.

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“…Regarding the validity, it is generally recommended to prepare the training data annotation with many pathologists as possible to minimize inter-observer variation. In addition, it is also recommended to perform external cross-validation using datasets from external institutions or publicly available data such as The Cancer Genome Atlas (TCGA) or GTEx Histological Images [ 66 ]. In this context, only Kather et al study used external validation dataset from the Darmkrebs Chancen der Verhütung durch Screening (DACHS) study to validate the performance of their model [ 45 ].…”

Section: Discussionmentioning

confidence: 99%

Current Trends of Artificial Intelligence for Colorectal Cancer Pathology Image Analysis: A Systematic Review

Thakur

Yoon²,

Chong

2020

Cancers

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Colorectal cancer (CRC) is one of the most common cancers requiring early pathologic diagnosis using colonoscopy biopsy samples. Recently, artificial intelligence (AI) has made significant progress and shown promising results in the field of medicine despite several limitations. We performed a systematic review of AI use in CRC pathology image analysis to visualize the state-of-the-art. Studies published between January 2000 and January 2020 were searched in major online databases including MEDLINE (PubMed, Cochrane Library, and EMBASE). Query terms included “colorectal neoplasm,” “histology,” and “artificial intelligence.” Of 9000 identified studies, only 30 studies consisting of 40 models were selected for review. The algorithm features of the models were gland segmentation (n = 25, 62%), tumor classification (n = 8, 20%), tumor microenvironment characterization (n = 4, 10%), and prognosis prediction (n = 3, 8%). Only 20 gland segmentation models met the criteria for quantitative analysis, and the model proposed by Ding et al. (2019) performed the best. Studies with other features were in the elementary stage, although most showed impressive results. Overall, the state-of-the-art is promising for CRC pathological analysis. However, datasets in most studies had relatively limited scale and quality for clinical application of this technique. Future studies with larger datasets and high-quality annotations are required for routine practice-level validation.

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Atlas of Digital Pathology: A Generalized Hierarchical Histological Tissue Type-Annotated Database for Deep Learning

Cited by 54 publications

References 38 publications

Aggregation of cohorts for histopathological diagnosis with deep morphological analysis

Aggregation of cohorts for histopathological diagnosis with deep morphological analysis

Assessment of glomerular morphological patterns by deep learning algorithms

Current Trends of Artificial Intelligence for Colorectal Cancer Pathology Image Analysis: A Systematic Review

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