Weakly supervised object localization and detection: A survey
As an emerging and challenging problem in the computer vision community, weakly
supervised object localization and detection plays an important role for developing new …
supervised object localization and detection plays an important role for developing new …
Deep learning for brain tumor segmentation: a survey of state-of-the-art
Quantitative analysis of the brain tumors provides valuable information for understanding the
tumor characteristics and treatment planning better. The accurate segmentation of lesions …
tumor characteristics and treatment planning better. The accurate segmentation of lesions …
Weakly supervised segmentation of COVID19 infection with scribble annotation on CT images
Segmentation of infections from CT scans is important for accurate diagnosis and follow-up
in tackling the COVID-19. Although the convolutional neural network has great potential to …
in tackling the COVID-19. Although the convolutional neural network has great potential to …
An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization
Medical images differ from natural images in significantly higher resolutions and smaller
regions of interest. Because of these differences, neural network architectures that work well …
regions of interest. Because of these differences, neural network architectures that work well …
Medical image segmentation with 3D convolutional neural networks: A survey
Computer-aided medical image analysis plays a significant role in assisting medical
practitioners for expert clinical diagnosis and deciding the optimal treatment plan. At present …
practitioners for expert clinical diagnosis and deciding the optimal treatment plan. At present …
Deep learning for weakly-supervised object detection and localization: A survey
Abstract Weakly-Supervised Object Detection (WSOD) and Localization (WSOL), ie.,
detecting multiple and single instances with bounding boxes in an image using image-level …
detecting multiple and single instances with bounding boxes in an image using image-level …
Source-free domain adaptation for image segmentation
Abstract Domain adaptation (DA) has drawn high interest for its capacity to adapt a model
trained on labeled source data to perform well on unlabeled or weakly labeled target data …
trained on labeled source data to perform well on unlabeled or weakly labeled target data …
Bounding boxes for weakly supervised segmentation: Global constraints get close to full supervision
We propose a novel weakly supervised learning segmentation based on several global
constraints derived from box annotations. Particularly, we leverage a classical tightness prior …
constraints derived from box annotations. Particularly, we leverage a classical tightness prior …
Weakly supervised segmentation with cross-modality equivariant constraints
Weakly supervised learning has emerged as an appealing alternative to alleviate the need
for large labeled datasets in semantic segmentation. Most current approaches exploit class …
for large labeled datasets in semantic segmentation. Most current approaches exploit class …
Weakly Supervised Deep Learning in Radiology
Deep learning (DL) is currently the standard artificial intelligence tool for computer-based
image analysis in radiology. Traditionally, DL models have been trained with strongly …
image analysis in radiology. Traditionally, DL models have been trained with strongly …