“… Lazard et al. 1 predict homologous recombination deficiency from hematoxylin and eosin-stained slides of breast cancer tissue using deep learning. By controlling for technical artifacts on a curated dataset, the model puts forward novel HRD-related morphologies in luminal breast cancers.…”
mentioning
confidence: 99%
“…Lazard et al. 1 investigate the performance of artificial intelligence (AI) methods to predict HRD from hematoxylin and eosin-stained (H&E) whole-slide images (WSIs) of breast cancer (BC) tissue and describe novel HRD-related morphological patterns in clinically relevant luminal BC patients ( Figure 1 ). Figure 1 Interplay of PARPi response, genotype and genomic phenotype, confounding factors, and resulting morphology The confounders may be correlated with the genotype and used by the AI model.…”
mentioning
confidence: 99%
“…Lazard et al. 1 partly block the technical confounders by focusing on a single-center dataset, controlling for technical artifacts. Additionally, they partly block the possibility of using biological confounders by modeling only basal BC samples.…”
mentioning
confidence: 99%
“…Lazard et al. 1 show that spuriosity manifests in TCGA, besides the known site-related technical artifacts, through the correlation between molecular subtype and HRD, such that subtype-specific morphologies, unrelated to HRD, may be learned. Even their curated private dataset contains artifacts related to a change in fixation and impregnation protocols over the collection period which can indicate the time of sample collection and be correlated with a changing patient demographic.…”
mentioning
confidence: 99%
“…Lazard et al. 1 exemplify these methods, shed new light on morphologies that are indicative of HRD in luminal BC, and set the stage for further work to validate the clinical applicability of H&E WSI-based deep learning methods to be used as a complementary diagnostic tool to perform pre-screening for HRD.…”
“… Lazard et al. 1 predict homologous recombination deficiency from hematoxylin and eosin-stained slides of breast cancer tissue using deep learning. By controlling for technical artifacts on a curated dataset, the model puts forward novel HRD-related morphologies in luminal breast cancers.…”
mentioning
confidence: 99%
“…Lazard et al. 1 investigate the performance of artificial intelligence (AI) methods to predict HRD from hematoxylin and eosin-stained (H&E) whole-slide images (WSIs) of breast cancer (BC) tissue and describe novel HRD-related morphological patterns in clinically relevant luminal BC patients ( Figure 1 ). Figure 1 Interplay of PARPi response, genotype and genomic phenotype, confounding factors, and resulting morphology The confounders may be correlated with the genotype and used by the AI model.…”
mentioning
confidence: 99%
“…Lazard et al. 1 partly block the technical confounders by focusing on a single-center dataset, controlling for technical artifacts. Additionally, they partly block the possibility of using biological confounders by modeling only basal BC samples.…”
mentioning
confidence: 99%
“…Lazard et al. 1 show that spuriosity manifests in TCGA, besides the known site-related technical artifacts, through the correlation between molecular subtype and HRD, such that subtype-specific morphologies, unrelated to HRD, may be learned. Even their curated private dataset contains artifacts related to a change in fixation and impregnation protocols over the collection period which can indicate the time of sample collection and be correlated with a changing patient demographic.…”
mentioning
confidence: 99%
“…Lazard et al. 1 exemplify these methods, shed new light on morphologies that are indicative of HRD in luminal BC, and set the stage for further work to validate the clinical applicability of H&E WSI-based deep learning methods to be used as a complementary diagnostic tool to perform pre-screening for HRD.…”
In computational pathology, the application of Deep Learning to the analysis of Whole Slide Images (WSI) has provided results of unprecedented quality. Due to their enormous size, WSIs have to be split into small images (tiles) which are first encoded and whose representations are then agglomerated in order to solve prediction tasks, such as prognosis or treatment response. The choice of the encoding strategy plays a key role in such algorithms. Current approaches include the use of encodings trained on unrelated data sources, full supervision or self-supervision. In particular, self-supervised learning (SSL) offers a great opportunity to exploit all the unlabelled data available. However, it often requires large computational resources and can be challenging to train. On the other end of the spectrum, fully-supervised methods make use of valuable prior knowledge about the data but involve a costly amount of expert time. This paper proposes a framework to reconcile SSL and full supervision and measures the tradeoff between long SSL training and annotation effort, showing that a combination of both has the potential to substantially increase performance. On a recently organized challenge on grading Cervical Biopsies, we show that our mixed supervision scheme reaches high performance (weighted accuracy (WA): 0.945), outperforming both SSL (WA: 0.927) and transfer learning from ImageNet (WA: 0.877). We further provide insights and guidelines to train a clinically impactful classifier with a limited expert and/or computational workload budget. We expect that the combination of full and self-supervision is an interesting strategy for many tasks in computational pathology and will be widely adopted by the field.
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