The surgical treatment of localized breast cancer has become progressively less aggressive over the years.The management of the axillary lymph nodes has been modified by the introduction of sentinel lymph node biopsy....The surgical treatment of localized breast cancer has become progressively less aggressive over the years.The management of the axillary lymph nodes has been modified by the introduction of sentinel lymph node biopsy. Axillary dissection can be avoided in patients with sentinel lymph node negative biopsies. Based on randomized trials data, it has been proposed that no lymph node dissection should be carried out even in certain patients with sentinel lymph node positive biopsies. This commentary discusses the basis of such recommendations and cautions against a general omission of lymph node dissection in breast cancer patients with positive sentinel lymph node biopsies. Instead, an individualized approach based on axillary tumor burden and biology of the cancer should be considered.展开更多
Few-shot learning is becoming more and more popular in many fields,especially in the computer vision field.This inspires us to introduce few-shot learning to the genomic field,which faces a typical few-shot problem be...Few-shot learning is becoming more and more popular in many fields,especially in the computer vision field.This inspires us to introduce few-shot learning to the genomic field,which faces a typical few-shot problem because some tasks only have a limited number of samples with high-dimensions.The goal of this study was to investigate the few-shot disease sub-type prediction problem and identify patient subgroups through training on small data.Accurate disease subtype classification allows clinicians to efficiently deliver investigations and interventions in clinical practice.We propose the SW-Net,which simulates the clinical process of extracting the shared knowledge from a range of interrelated tasks and generalizes it to unseen data.Our model is built upon a simple baseline,and we modified it for genomic data.Supportbased initialization for the classifier and transductive fine-tuning techniques were applied in our model to improve prediction accuracy,and an Entropy regularization term on the query set was appended to reduce over-fitting.Moreover,to address the high dimension and high noise issue,we future extended a feature selection module to adaptively select important features and a sample weighting module to prioritize high-confidence samples.Experiments on simulated data and The Cancer Genome Atlas meta-dataset show that our new baseline model gets higher prediction accuracy compared to other competing algorithms.展开更多
文摘The surgical treatment of localized breast cancer has become progressively less aggressive over the years.The management of the axillary lymph nodes has been modified by the introduction of sentinel lymph node biopsy. Axillary dissection can be avoided in patients with sentinel lymph node negative biopsies. Based on randomized trials data, it has been proposed that no lymph node dissection should be carried out even in certain patients with sentinel lymph node positive biopsies. This commentary discusses the basis of such recommendations and cautions against a general omission of lymph node dissection in breast cancer patients with positive sentinel lymph node biopsies. Instead, an individualized approach based on axillary tumor burden and biology of the cancer should be considered.
基金supported by the Macao Science and Technology Development Funds Grands No.0158/2019/A3 from the Macao Special Administrative Region of the People’s Republic of China.
文摘Few-shot learning is becoming more and more popular in many fields,especially in the computer vision field.This inspires us to introduce few-shot learning to the genomic field,which faces a typical few-shot problem because some tasks only have a limited number of samples with high-dimensions.The goal of this study was to investigate the few-shot disease sub-type prediction problem and identify patient subgroups through training on small data.Accurate disease subtype classification allows clinicians to efficiently deliver investigations and interventions in clinical practice.We propose the SW-Net,which simulates the clinical process of extracting the shared knowledge from a range of interrelated tasks and generalizes it to unseen data.Our model is built upon a simple baseline,and we modified it for genomic data.Supportbased initialization for the classifier and transductive fine-tuning techniques were applied in our model to improve prediction accuracy,and an Entropy regularization term on the query set was appended to reduce over-fitting.Moreover,to address the high dimension and high noise issue,we future extended a feature selection module to adaptively select important features and a sample weighting module to prioritize high-confidence samples.Experiments on simulated data and The Cancer Genome Atlas meta-dataset show that our new baseline model gets higher prediction accuracy compared to other competing algorithms.
