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Ductal Carcinoma in Situ (DCIS) constitutes 20–25% of all diagnosed breast cancers and is a well known potential precursor for invasive breast cancer.1 The gold standard method for diagnosing DCIS involves the detection of calcifications and abnormal cell proliferation in mammary ducts in Hematoxylin and Eosin (H&E) stained whole-slide images (WSIs). Automatic duct detection may facilitate this task as well as downstream applications that currently require tedious, manual annotation of ducts. Examples of these are grading of DCIS lesions2 and prediction of local recurrence of DCIS.3 Several methods have been developed for object detection in the field of deep learning. Such models are typically initialised using ImageNet transfer-learning features, as the limited availability of annotated medical images has hindered the creation of domain-specific encoders. Novel techniques such as self-supervised learning (SSL) promise to overcome this problem by utilising unlabelled data to learn feature encoders. SSL encoders trained on unlabelled ImageNet have demonstrated SSL’s capacity to produce meaningful representations, scoring higher than supervised features on the ImageNet 1% classification task.4 In the domain of histopathology, feature encoders (Histo encoders) have been developed.5, 6 In classification experiments with linear regression, frozen features of these encoders outperformed those of ImageNet encoders. However, when models initialised with histopathology and ImageNet encoders were fine-tuned on the same classification tasks, there were no differences in performance between the encoders.5, 6 Furthermore, the transferability of SSL encodings to object detection is poorly understood.4 These findings show that more research is needed to develop training strategies for SSL encoders that can enhance performance in relevant downstream tasks. In our study, we investigated whether current state-of-the-art SSL methods can provide model initialisations that outperform ImageNet pre-training on the task of duct detection in WSIs of breast tissue resections. We compared the performance of these SSL-based histopathology encodings (Histo-SSL) with ImageNet pre-training (supervised and self-supervised) and training from scratch. Additionally, we compared the performance of our Histo-SSL encodings with published Histo encoders by Ciga5 and Mormont6 on the same task.
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