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

Correlation between Pathologic Complete Response in the Breast and Absence of Axillary Lymph Node Metastases after Neoadjuvant Systemic Therapy

Objective:The aim was to investigate whether pathologic complete response (PCR) in the breast is correlated with absence of axillary lymph node metastases at final pathology (ypN0) in patients treated with neoadjuvant systemic therapy (NST) for different breast cancer subtypes.Background:Pathologic complete response rates have improved on account of more effective systemic treatment regimens. Promising results in feasibility trials with percutaneous image-guided tissue sampling for the identification of breast PCR after NST raise the question whether breast surgery is a redundant procedure. Thereby, the need for axillary surgery should be reconsidered as well.Methods:Patients diagnosed with cT1-3N0-1 breast cancer and treated with NST, followed by surgery between 2010 and 2016, were selected from the Netherlands Cancer Registry. Patients were compared according to the pa

Risk of regional recurrence in triple-negative breast cancer patients: a Dutch cohort study

Triple-negative breast cancer is associated with early recurrence and low survival rates. Several trials investigate the safety of a more conservative approach of axillary treatment in clinically T1-2N0 breast cancer. Triple-negative breast cancer comprises only 15 % of newly diagnosed breast cancers, which might result in insufficient power for representative results for this subgroup. We aimed to provide a nationwide overview on the occurrence of (regional) recurrences in triple-negative breast cancer patients with a clinically T1-2N0 status. For this cohort study, 2548 women diagnosed between 2005 and 2008 with clinically T1-2N0 triple-negative breast cancer were selected from the Netherlands Cancer Registry. Follow-up data until 2014 were analyzed using Kaplan–Meier. Sentinel lymph node biopsy was performed in 2486 patients, and (completion) axillary lymph node dissection in 562 patients. Final pathologic nodal status was pN0 in 78.5 %, pN1mi in 4.5 %, pN1 in 12.3 %, pN2–3 in 3.6 %, and pNx in 1.1 %. During a follow-up of 5 years, regional recurrence occurred in 2.9 %, local recurrence in 4.2 % and distant recurrence in 12.2 %. Five-year disease-free survival was 78.7 %, distant disease-free survival 80.5 %, and 5-year overall survival 82.3 %. Triple-negative clinically T1-2N0 breast cancer patients rarely develop a regional recurrence. Their disease-free survival is more threatened by distant recurrence, affecting their overall survival. Consequently, it seems justified to include triple-negative breast cancer patients in randomized controlled trials investigating the safety of minimizing axillary staging and treatment

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

Ubicación y peso de Micelio de Sclerotinia sclerotiorum para producir infeccion en lechuga (Lactuca sativa)

p.85-88El objetivo del presente trabajo es evaluar la distancia crítica para la inoculación del micelio de Sclerotinia sclerotiorum al cuello de la planta de lechuga (Lactuca sativa) y el peso del mismo para producir infección y caída de las plántulas en cámara de cultivo. La mayor cantidad de plantas caídas se obtuvo con 0,7 y 2,8 grs de inoculo (masa miceliar) ubicado junto al cuello de la planta. Estos resultados pueden ser de utilidad para estudios acerca del control cultural, químico o biológico de la podredumbre ocasionada por S. sclerotiorum en lechuga