scholarly journals AAWS-Net: Anatomy-aware weakly-supervised learning network for breast mass segmentation

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256830
Author(s):  
Yeheng Sun ◽  
Yule Ji
Keyword(s):  
Supervised Learning ◽  
Learning Strategy ◽  
Medical Image Analysis ◽  
Training Data ◽  
Breast Mass ◽  
Weakly Supervised Learning ◽  
Learning Network ◽  
Teacher Student ◽  
Mass Segmentation ◽  
Weakly Supervised

Accurate segmentation of breast masses is an essential step in computer aided diagnosis of breast cancer. The scarcity of annotated training data greatly hinders the model’s generalization ability, especially for the deep learning based methods. However, high-quality image-level annotations are time-consuming and cumbersome in medical image analysis scenarios. In addition, a large amount of weak annotations is under-utilized which comprise common anatomy features. To this end, inspired by teacher-student networks, we propose an Anatomy-Aware Weakly-Supervised learning Network (AAWS-Net) for extracting useful information from mammograms with weak annotations for efficient and accurate breast mass segmentation. Specifically, we adopt a weakly-supervised learning strategy in the Teacher to extract anatomy structure from mammograms with weak annotations by reconstructing the original image. Besides, knowledge distillation is used to suggest morphological differences between benign and malignant masses. Moreover, the prior knowledge learned from the Teacher is introduced to the Student in an end-to-end way, which improves the ability of the student network to locate and segment masses. Experiments on CBIS-DDSM have shown that our method yields promising performance compared with state-of-the-art alternative models for breast mass segmentation in terms of segmentation accuracy and IoU.

Weakly Supervised Learning Strategy for Lung Defect Segmentation

2019 ◽  
pp. 541-548
Author(s):  
Robin Sandkühler ◽  
Christoph Jud ◽  
Grzegorz Bauman ◽  
Corin Willers ◽  
Orso Pusterla ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Learning Strategy ◽  
Weakly Supervised Learning ◽  
Weakly Supervised

scholarly journals Weakly Supervised Learning for Categorization of Medical Inquiries for Customer Service Effectiveness

2021 ◽  
Vol 6 ◽  
Author(s):  
Shikha Singhal ◽  
Bharat Hegde ◽  
Prathamesh Karmalkar ◽  
Justna Muhith ◽  
Harsha Gurulingappa
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Supervised Learning ◽  
Language Processing ◽  
Medical Information ◽  
Service Providers ◽  
Training Data ◽  
Weakly Supervised Learning ◽  
Weakly Supervised

With the growing unstructured data in healthcare and pharmaceutical, there has been a drastic adoption of natural language processing for generating actionable insights from text data sources. One of the key areas of our exploration is the Medical Information function within our organization. We receive a significant amount of medical information inquires in the form of unstructured text. An enterprise-level solution must deal with medical information interactions via multiple communication channels which are always nuanced with a variety of keywords and emotions that are unique to the pharmaceutical industry. There is a strong need for an effective solution to leverage the contextual knowledge of the medical information business along with digital tenants of natural language processing (NLP) and machine learning to build an automated and scalable process that generates real-time insights on conversation categories. The traditional supervised learning methods rely on a huge set of manually labeled training data and this dataset is difficult to attain due to high labeling costs. Thus, the solution is incomplete without its ability to self-learn and improve. This necessitates techniques to automatically build relevant training data using a weakly supervised approach from textual inquiries across consumers, healthcare professionals, sales, and service providers. The solution has two fundamental layers of NLP and machine learning. The first layer leverages heuristics and knowledgebase to identify the potential categories and build an annotated training data. The second layer, based on machine learning and deep learning, utilizes the training data generated using the heuristic approach for identifying categories and sub-categories associated with verbatim. Here, we present a novel approach harnessing the power of weakly supervised learning combined with multi-class classification for improved categorization of medical information inquiries.

Shape robust Siamese network tracking based on weakly supervised learning

2020 ◽  
pp. 2050057
Author(s):  
Ding Ma ◽  
Yong Zhou ◽  
Rui Yao ◽  
Jiaqi Zhao ◽  
Bing Liu ◽  
...  
Keyword(s):  
Target Tracking ◽  
Supervised Learning ◽  
Main Idea ◽  
Processing Method ◽  
Training Data ◽  
Tracking Accuracy ◽  
Weakly Supervised Learning ◽  
Siamese Network ◽  
Monitoring Framework ◽  
Weakly Supervised

This paper combines the boundary box regression with the training data occlusion processing method, the occlusion problem is more accurate and the tracking accuracy is improved. The occlusion problem is now the major challenge in target tracking. This paper puts forward a weakly monitoring framework to address this problem. The main idea is to randomly hide the most discriminating patches in the input images, forcing the network to focus on other relevant parts. Our method only needs to modify the inputs, no need to hide any patches during the test.

scholarly journals Adversarial Label Learning

2019 ◽  
Vol 33 ◽  
pp. 3183-3190
Author(s):  
Chidubem Arachie ◽  
Bert Huang
Keyword(s):  
Supervised Learning ◽  
Error Rate ◽  
Upper Bound ◽  
Training Data ◽  
Weakly Supervised Learning ◽  
Weak Supervision ◽  
Primal Dual ◽  
Weakly Supervised

We consider the task of training classifiers without labels. We propose a weakly supervised method—adversarial label learning—that trains classifiers to perform well against an adversary that chooses labels for training data. The weak supervision constrains what labels the adversary can choose. The method therefore minimizes an upper bound of the classifier’s error rate using projected primal-dual subgradient descent. Minimizing this bound protects against bias and dependencies in the weak supervision. Experiments on real datasets show that our method can train without labels and outperforms other approaches for weakly supervised learning.

scholarly journals Weakly Supervised Learning Creates a Fusion of Modeling Cultures

2021 ◽  
Vol 7 (1) ◽  
pp. 203-211
Author(s):  
Chengliang Tang ◽  
Gan Yuan ◽  
Tian Zheng
Keyword(s):  
Supervised Learning ◽  
Weakly Supervised Learning ◽  
Weakly Supervised
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