MSCDA: Multi-level Semantic-guided Contrast Improves Unsupervised Domain Adaptation for Breast MRI Segmentation in Small Datasets

Deep learning (DL) applied to breast tissue segmentation in magnetic resonance imaging (MRI) has received increased attention in the last decade, however, the domain shift which arises from different vendors, acquisition protocols, and biological heterogeneity, remains an important but challenging obstacle on the path towards clinical implementation. In this paper, we propose a novel Multi-level Semantic-guided Contrastive Domain Adaptation (MSCDA) framework to address this issue in an unsupervised manner. Our approach incorporates self-training with contrastive learning to align feature representations between domains. In particular, we extend the contrastive loss by incorporating pixel-to-pixel, pixel-to-centroid, and centroid-to-centroid contrasts to better exploit the underlying semantic information of the image at different levels. To resolve the data imbalance problem, we utilize a category-wise cross-domain sampling strategy to sample anchors from target images and build a hybrid memory bank to store samples from source images. We have validated MSCDA with a challenging task of cross-domain breast MRI segmentation between datasets of healthy volunteers and invasive breast cancer patients. Extensive experiments show that MSCDA effectively improves the model's feature alignment capabilities between domains, outperforming state-of-the-art methods. Furthermore, the framework is shown to be label-efficient, achieving good performance with a smaller source dataset. The code is publicly available at \url{https://github.com/ShengKuangCN/MSCDA}.Comment: 17 pages, 8 figure

MSCDA: Multi-level semantic-guided contrast improves unsupervised domain adaptation for breast MRI segmentation in small datasets

Deep learning (DL) applied to breast tissue segmentation in magnetic resonance imaging (MRI) has received increased attention in the last decade, however, the domain shift which arises from different vendors, acquisition protocols, and biological heterogeneity, remains an important but challenging obstacle on the path towards clinical implementation. In this paper, we propose a novel Multi-level Semantic-guided Contrastive Domain Adaptation (MSCDA) framework to address this issue in an unsupervised manner. Our approach incorporates self-training with contrastive learning to align feature representations between domains. In particular, we extend the contrastive loss by incorporating pixel-to-pixel, pixel-to-centroid, and centroid-to-centroid contrasts to better exploit the underlying semantic information of the image at different levels. To resolve the data imbalance problem, we utilize a category-wise cross-domain sampling strategy to sample anchors from target images and build a hybrid memory bank to store samples from source images. We have validated MSCDA with a challenging task of cross-domain breast MRI segmentation between datasets of healthy volunteers and invasive breast cancer patients. Extensive experiments show that MSCDA effectively improves the model's feature alignment capabilities between domains, outperforming state-of-the-art methods. Furthermore, the framework is shown to be label-efficient, achieving good performance with a smaller source dataset. The code is publicly available at https://github.com/ShengKuangCN/MSCDA

Determinants of expression of SARS-CoV-2 entry-related genes in upper and lower airways.

Pathogenesis and treatment of chronic pulmonary disease

Determinants of SARS-CoV-2 receptor gene expression in upper and lower airways

The recent outbreak of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19), has led to a worldwide pandemic. One week after initial symptoms develop, a subset of patients progresses to severe disease, with high mortality and limited treatment options. To design novel interventions aimed at preventing spread of the virus and reducing progression to severe disease, detailed knowledge of the cell types and regulating factors driving cellular entry is urgently needed. Here we assess the expression patterns in genes required for COVID-19 entry into cells and replication, and their regulation by genetic, epigenetic and environmental factors, throughout the respiratory tract using samples collected from the upper (nasal) and lower airways (bronchi). Matched samples from the upper and lower airways show a clear increased expression of these genes in the nose compared to the bronchi and parenchyma. Cellular deconvolution indicates a clear association of these genes with the proportion of secretory epithelial cells. Smoking status was found to increase the majority of COVID-19 related genes including ACE2 and TMPRSS2 but only in the lower airways, which was associated with a significant increase in the predicted proportion of goblet cells in bronchial samples of current smokers. Both acute and second hand smoke were found to increase ACE2 expression in the bronchus. Inhaled corticosteroids decrease ACE2 expression in the lower airways. No significant effect of genetics on ACE2 expression was observed, but a strong association of DNA- methylation with ACE2 and TMPRSS2- mRNA expression was identified in the bronchus.</p

Determinants of expression of SARS-CoV-2 entry-related genes in upper and lower airways.

Funder: Dutch Research Council (NWO)Funder: Cancer Research UK Cambridge CentreFunder: ATS Foundation/Boehringer Ingelheim Pharmaceuticals Inc. Research FellowshipFunder: The Netherlands Ministry of Spatial Planning, Housing, and the EnvironmentFunder: Chan Zuckerberg InitiativeFunder: The Netherlands Ministry of Health, Welfare, and SportFunder: Longfonds Junior FellowshipFunder: Cambridge BioresourceFunder: The Netherlands Organization for Health Research and DevelopmentFunder: Cambridge NIHR Biomedical Research CentreFunder: Parker B. Francis FellowshipFunder: China Scholarship Counci

Transcendental-Phenomenological Proof and Descriptive Metaphysics

Following P.F. Strawson's reading of Kant, the majority of the literature on transcendental arguments seeks to divorce such arguments from their original Kantian context. This thesis is concerned with Mark Sacks's recent defence of transcendental arguments, which takes a different approach. A critique is given of Sacks's work and extensions and modifications of his approach are recommended. It is proposed that certain difficulties encountered by Kant's transcendentally-ideal approach can be overcome with Hegelian solutions

Excision of both pretreatment marked positive nodes and sentinel nodes improves axillary staging after neoadjuvant systemic therapy in breast cancer

Background: Marking the axilla with radioactive iodine seed and sentinel lymph node (SLN) biopsy have been proposed for axillary staging after neoadjuvant systemic therapy in clinically node-positive breast cancer. This study evaluated the identification rate and detection of residual disease with combined excision of pretreatment-positive marked lymph nodes (MLNs) together with SLNs. Methods: This was a multicentre retrospective analysis of patients with clinically node-positive breast cancer undergoing neoadjuvant systemic therapy and the combination procedure (with or without axillary lymph node dissection). The identification rate and detection of axillary residual disease were calculated for the combination procedure, and for MLNs and SLNs separately. Results: At least one MLN and/or SLN(s) were identified by the combination procedure in 138 of 139 patients (identification rate 99·3 per cent). The identification rate was 92·8 per cent for MLNs alone and 87·8 per cent for SLNs alone. In 88 of 139 patients (63·3 per cent) residual axillary disease was detected by the combination procedure. Residual disease was shown only in the MLN in 20 of 88 patients (23 per cent) and only in the SLN in ten of 88 (11 per cent), whereas both the MLN and SLN contained residual disease in the remainder (58 of 88, 66 per cent). Conclusion: Excision of the pretreatment-positive MLN together with SLNs after neoadjuvant systemic therapy in patients with clinically node-positive disease resulted in a higher identification rate and improved detection of residual axillary disease