… to focus on weakly supervised learning for medical image … process of weakly supervised
learning in medical image analysis, including incomplete, inexact, and inaccurate supervision, …

… , and loss functions for supervised learning. For weakly supervised learning, we analyze
the collected literature using transfer learning and data augmentation techniques. Our review …

… How to design the weakly supervised learning methods … weakly supervised approach in
this article for fast and effective classification on the whole slide lung cancer images. Our method …

… both deep neural networks and other machine learning methods (… segmentation, though the
methods developed are general … We are motivated to assess weakly supervised methods in …

… a weakly supervised deep learning strategy for detecting and classifying COVID-19 infection
from CT images. The proposed method … developed technique in large-scale clinical studies. …

… new predictive methods remains an urgent unmet medical need. This study identifies an
effective biomarker and presents an automatic weakly supervised deep learning framework for …

… Methods We propose a new efficient deep learning method to accurately segment targets …
while generating an annotated dataset for deep learning methods. It involves a generative …

… Weakly supervised learning of object detection is … deep convolutional neural networks
pre-trained on large-scale image-level classification tasks. We propose a weakly supervised deep …

… Thus, weakly supervised learning can unlock a large amount … key concepts in weakly
supervised learning and provide an … translational work, weakly supervised learning could facilitate …

… Large-scale datasets have driven the rapid development of deep neural networks for visual
… To address this drawback we propose an end-to-end weakly-supervised deep learning …