22 papers · ranked by Valyu relevance
Rafael Poyiadzi, Daniel Bacaicoa-Barber, Jesús Cid‐Sueiro, Miquel Perelló-Nieto + 2 more
'Miquel Perelló-Nieto' 'Peter Flach' 'Raúl Santos‐Rodríguez'] Abstract—Many ways of annotating a dataset for machine learning classification tasks that go beyond the usual class labels exist in practice. These are of interest as they can simplify or facilitate the collection of annotations, while not greatly affecting…
Jason A. Fries, Paroma Varma, Vincent S. Chen, Ke Xiao + 10 more
Recent releases of population-scale biomedical repositories such as the UK Biobank have enabled unprecedented access to prospectively collected medical imaging data. Applying machine learning methods to analyze these data holds great promise in facilitating new insights into the genetic and epidemiological associations…
Dingwen Zhang, Junwei Han, Gong Cheng, Ming–Hsuan Yang
—As an emerging and challenging problem in the computer vision community, weakly supervised object localization and detection plays an important role for developing new generation computer vision systems and has received significant attention in the past decade. As methods have been proposed, a comprehensive survey of…
Zi-Hao Zhou, Jun-Jie Wang, Tong Wei, Min-Ling Zhang
Contrastive learning has achieved remarkable success in learning effective representations, with supervised contrastive learning often outperforming self-supervised approaches. However, in realworld scenarios, data annotations are often ambiguous or inaccurate, meaning that class labels may not reliably indicate…
Suzanna Cuypers, Maarten Bassier, Maarten Vergauwen, Sylvie Le Hegarat-Mascle
'Sylvie Le Hegarat-Mascle'] Recent advances in deep learning models for image interpretation finally made it possible to automate construction site monitoring processes that rely on remote sensing. However, the major drawback of these models is their dependency on large datasets of training images labeled at pixel…
Samantha Biegel, Rafah El-Khatib, Luiz Otávio V. B. Oliveira, Max Baak + 1 more
'Max Baak' 'Nanne Aben'] The availability of labelled data is one of the main limitations in machine learning. We can alleviate this using weak supervision: a framework that uses expertdefined rules λ to estimate probabilistic labels p(y|λ) for the entire data set. These rules, however, are dependent on what experts…
Wang Jia-jie, Jiangchao Yao, Ya Zhang, Rui Zhang
Weakly supervised object detection has recently received much attention, since it only requires imagelevel labels instead of the bounding-box labels consumed in strongly supervised learning. Nevertheless, the save in labeling expense is usually at the cost of model accuracy. In this paper, we propose a simple but…
Xueting Ren, Liye Jia, Zijuan Zhao, Yan Qiang + 4 more
'Juanjuan Zhao' 'Jingyu Sun'] Aiming at the problems of long time, high cost, invasive sampling damage, and easy emergence of drug resistance in lung cancer gene detection, a reliable and non-invasive prognostic method is proposed. Under the guidance of weakly supervised learning, deep metric learning and graph…
Matthew Brendel, Vanesa Getseva, Majd Al Assaad, Michael Sigouros + 6 more
Estimating tumor purity is especially important in the age of precision medicine. Purity estimates have been shown to be critical for correction of tumor sequencing results, and higher purity samples allow for more accurate interpretations from next-generation sequencing results. In addition, tumor purity has been…
Robert John O’Shea, Carolyn Horst, Thubeena Manickavasagar, Daniel Hughes + 4 more
Explainability is a major limitation of current convolutional neural network (CNN) image classifiers. A CNN is required which supports its image-level prediction with a voxel-level segmentation. A weakly-supervised Unet architecture (WSUnet) is proposed to model voxel classes, by training with image-level supervision.…
Jared A. Dunnmon, Alexander J. Ratner, Khaled Saab, Nishith Khandwala + 7 more
'Nishith Khandwala' 'Matthew Markert' 'Hersh Sagreiya' 'Roger Goldman' 'Christopher Lee-Messer' 'Matthew P. Lungren' 'Daniel L. Rubin' 'Christopher Ré'] Title: Summary A major bottleneck in developing clinically impactful machine learning models is a lack of labeled training data for model supervision. Thus, medical…
Xianming Liu, Amy Zhang, Tobias Tiecke, Andreas Gros + 1 more
'Thomas S. Huang'] Learning from weakly-supervised data is one of the main challenges in machine learning and computer vision, especially for tasks such as image semantic segmentation where labeling is extremely expensive and subjective. In this paper, we propose a novel neural network architecture to perform…
Romane Dubois, Lydia Bousset, Stéphane Jumel, Melen Leclerc + 2 more
Accurate segmentation of plant disease symptoms is essential for crop monitoring and phenotyping, yet it typically requires costly pixel-level annotations. Weakly supervised semantic segmentation (WSSS) alleviates this burden using image-level labels, but its performance depends on the quality of spatial priors such as…
Ivan R. Nabi, Ben Cardoen, Ismail M. Khater, Guang Gao + 2 more
'Timothy H. Wong' 'Ghassan Hamarneh'] Nabi and colleagues discuss supervision paradigms for biological discovery from super-resolution microscopy to enable AI-accelerated exploration of the nanoscale architecture of subcellular macromolecules and organelles.
Chi-Chung Chen, Yi-Chen Yeh, Matthew MY Lin, Chao-Yuan Yeh
A robust artificial intelligence-assisted workflow for tumor assessment in pathology requires not only accurate classification but also precise lesion localization. While current weakly supervised learning methods significantly reduce the need for extensive annotations and leverage large quantities of annotation-free…
Degaga Wolde Feyisa, Yehualashet Megersa Ayano, Taye Girma Debelee, Friedhelm Schwenker + 2 more
Pulmonary tuberculosis (PTB) is a bacterial infection that affects the lung. PTB remains one of the infectious diseases with the highest global mortalities. Chest radiography is a technique that is often employed in the diagnosis of PTB. Radiologists identify the severity and stage of PTB by inspecting radiographic…
Prem Shrestha, Nicholas Kuang, Ji Yu
Automated cell segmentation from optical microscopy images is usually the first step in the pipeline of single-cell analysis. Recently, deep-learning based algorithms have shown superior performances for the cell segmentation tasks. However, a disadvantage of deep-learning is the requirement for a large amount of…
Aitor González-Marfil, Estibaliz Gómez-de-Mariscal, Ignacio Arganda-Carreras
We present DINOSim, a novel approach leveraging the DINOv2 pretrained encoder for zero-shot object detection and segmentation in electron microscopy datasets. By exploiting semantic embeddings, DINOSim generates pseudo-labels from patch distances to a user-selected reference, which are subsequently employed in a…
Shahbaz Khan, Muhammad Tufail, Muhammad Tahir Khan, Zubair Ahmad Khan + 3 more
'Zubair Ahmad Khan' 'Javaid Iqbal' 'Mansoor Alam' 'Khanh N.Q. Le'] Excessive use of agrochemicals for weed controlling infestation has serious agronomic and environmental repercussions associated. An appropriate amount of pesticide/ chemicals is essential for achieving the desired smart farming and precision…
Hang Hu, Jyothsna Padmakumar Bindu, Julia Laskin
Mass spectrometry imaging (MSI) is widely used for the label-free molecular mapping of biological samples. The identification of co-localized molecules in MSI data is crucial to the understanding of biochemical pathways. However, complex MSI data are too large for manual annotation but too small for training deep…
Authors not listed
The concept of aClassical Structure provides a broad mathematical framework, whereas a Hyperstructure arises via the powerset construction, and an 𝑛-Superhyperstructure is obtained by iterating this construction n times [1]. Intuitively, the n-th powerset corresponds to 𝑛 successive applications of the powerset…
Hyuna Kwon, Zulfikhar Ali, Bryan Wong
Many per- and polyfluoroalkyl substances (PFASs) pose significant health hazards due to their bioactive and persistent bioaccumulative properties. However, assessing the bioactivities of PFASs is both time-consuming and costly due to the sheer number and expense of in vivo and in vitro biological experiments. To this…