Proactive Construction of an Annotated Imaging Database for Artificial Intelligence Training

Stadler, Caroline Bivik; Lindvall, Martin; Lundström, Claes; Bodén, Anna; Lindman, Karin; Rose, Jeronimo; Treanor, Darren; Blomma, Johan; Stacke, Karin; Pinchaud, Nicolas; Hedlund, Martin; Landgren, Filip; Woisetschläger, Mischa; Forsberg, Daniel

doi:10.1007/s10278-020-00384-4

Cited by 18 publications

(14 citation statements)

References 15 publications

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“…The validation and test set were sampled from the respective slides in a classbalanced way. For more information about the data collection and annotations, please see Stadler et al (2021).…”

Section: Datasetsmentioning

confidence: 99%

Learning Representations with Contrastive Self-Supervised Learning for Histopathology Applications

Stacke¹,

Unger²,

Lundström³

et al. 2021

Preprint

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Unsupervised learning has made substantial progress over the last few years, especially by means of contrastive self-supervised learning. The dominating dataset for benchmarking self-supervised learning has been ImageNet, for which recent methods are approaching the performance achieved by fully supervised training. The ImageNet dataset is however largely object-centric, and it is not clear yet what potential those methods have on widely different datasets and tasks that are not object-centric, such as in digital pathology. While self-supervised learning has started to be explored within this area with encouraging results, there is reason to look closer at how this setting differs from natural images and ImageNet. In this paper we make an in-depth analysis of contrastive learning for histopathology, pinpointing how the contrastive objective will behave differently due to the characteristics of histopathology data. We bring forward a number of considerations, such as view generation for the contrastive objective and hyper-parameter tuning. In a large battery of experiments, we analyze how the downstream performance in tissue classification will be affected by these considerations. The results point to how contrastive learning can reduce the annotation effort within digital pathology, but that the specific dataset characteristics need to be considered. To take full advantage of the contrastive learning objective, different calibrations of view generation and hyper-parameters are required. Our results pave the way for realizing the full potential of self-supervised learning for histopathology applications. Code and trained models are available at https://github.com/k-stacke/ssl-pathology.

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Section: Datasetsmentioning

confidence: 99%

Learning Representations with Contrastive Self-Supervised Learning for Histopathology Applications

Stacke¹,

Unger²,

Lundström³

et al. 2021

Preprint

Self Cite

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“…The publicly available test partition includes 11,888 images collected from seven publicly available datasets. The test partition includes WSIs (UNITOPATHO 31 , 32 , TCGA-COAD 33 , AIDA 34 , IMP-CRC 35 ) and cropped sections of WSIs (GlaS 36 , CRC 37 , UNITOPATHO 31 , 32 , Xu 38 ). Sections of WSIs are treated as WSIs, since they are provided with labels referring to the whole image.…”

Section: Resultsmentioning

confidence: 99%

Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotations

et al. 2022

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The digitalization of clinical workflows and the increasing performance of deep learning algorithms are paving the way towards new methods for tackling cancer diagnosis. However, the availability of medical specialists to annotate digitized images and free-text diagnostic reports does not scale with the need for large datasets required to train robust computer-aided diagnosis methods that can target the high variability of clinical cases and data produced. This work proposes and evaluates an approach to eliminate the need for manual annotations to train computer-aided diagnosis tools in digital pathology. The approach includes two components, to automatically extract semantically meaningful concepts from diagnostic reports and use them as weak labels to train convolutional neural networks (CNNs) for histopathology diagnosis. The approach is trained (through 10-fold cross-validation) on 3’769 clinical images and reports, provided by two hospitals and tested on over 11’000 images from private and publicly available datasets. The CNN, trained with automatically generated labels, is compared with the same architecture trained with manual labels. Results show that combining text analysis and end-to-end deep neural networks allows building computer-aided diagnosis tools that reach solid performance (micro-accuracy = 0.908 at image-level) based only on existing clinical data without the need for manual annotations.

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“…Training these networks, however, requires pixel-level annotations. The Diagnostic Reference Oncology Imaging Database (DROID) 20 , published in 2021, provides these pixel-level annotations as polygon annotations for 754 pathology images from multiple organs (ovarian, skin, breast, and colon).…”

Section: Background and Summarymentioning

confidence: 99%

Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset

Wilm¹,

Fragoso²,

Marzahl³

et al. 2022

Preprint

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Due to morphological similarities, the differentiation of histologic sections of cutaneous tumors into individual subtypes can be challenging. Recently, deep learning-based approaches have proven their potential for supporting pathologists in this regard. However, many of these supervised algorithms require a large amount of annotated data for robust development. We present a publicly available dataset consisting of 350 whole slide images of seven different canine cutaneous tumors complemented by 12,424 polygon annotations for 13 histologic classes including seven cutaneous tumor subtypes. Regarding sample size and annotation extent, this exceeds most publicly available datasets which are oftentimes limited to the tumor area or merely provide patch-level annotations. We validated our model for tissue segmentation, achieving a class-averaged Jaccard coefficient of 0.7047, and 0.9044 for tumor in particular. For tumor subtype classification, we achieve a slide-level accuracy of 0.9857. Since canine cutaneous tumors possess various histologic homologies to human tumors, we believe that the added value of this dataset is not limited to veterinary pathology but extends to more general fields of application.

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Proactive Construction of an Annotated Imaging Database for Artificial Intelligence Training

Cited by 18 publications

References 15 publications

Learning Representations with Contrastive Self-Supervised Learning for Histopathology Applications

Learning Representations with Contrastive Self-Supervised Learning for Histopathology Applications

Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotations

Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset

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