25 papers · ranked by Valyu relevance
Kazuki Takasan, Masaaki Iiyama, Lei Chu
Estimating fishing grounds is an important task in the fishing industry. This study modeled the fisher’s decision-making process based on sea surface temperature patterns as a pattern recognition task. We used a deep learning-based keypoint detector to estimate fishing ground locations from these patterns. However…
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…
Joshua Robinson, Stefanie Jegelka, Suvrit Sra
We study generalization properties of weakly supervised learning. That is, learning where only a few "strong" labels (the actual target of our prediction) are present but many more "weak" labels are available. In particular, we show that having access to weak labels can significantly accelerate the learning rate for…
Shuying Huang, Junpeng Li, Changchun Hua, Yana Yang
—To alleviate the annotation burden in supervised learning, N-tuples learning has recently emerged as a powerful weakly-supervised method. While existing N-tuples learning approaches extend pairwise learning to higher-order comparisons and accommodate various real-world scenarios, they often rely on task-specific…
Ke Li, Jitendra Malik
The scarcity of data annotated at the desired level of granularity is a recurring issue in many applications. Significant amounts of effort have been devoted to developing weakly supervised methods tailored to each individual setting, which are often carefully designed to take advantage of the particular properties of…
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…
Jiaxu Su, Junpeng Li, Changchun Hua, Yana Yang
Obtaining accurate class labels is often costly or unreliable, and may also be limited by privacy or other practical conditions. Compared with asking an annotator to provide the exact class, it is often easier to ask whether the true label belongs to a certain label subset. This query-response form defines a distinct…
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…
Shuai Yang, Ziyao Xing, Hengbin Wang, Xiang Gao + 7 more
Precisely discerning disease types and vulnerable areas is crucial in implementing effective monitoring of crop production. This forms the basis for generating targeted plant protection recommendations and automatic, precise applications. In this study, we constructed a dataset comprising six types of field maize leaf…
Hui Liu, Yang Zhang, Aichun Zhu, Zhiqiang Sun + 1 more
The deep learning-powered computational pathology has led to sig-nificant improvements in the speed and precise of tumor diagnosis,, while also exhibiting substantial potential to infer genetic mutations and gene expression levels. However,current studies remain limited in predicting molecular subtypes and recurrence…
Bodong Zhang, Hamid Manoochehri, Man Minh Ho, Fahimeh Fooladgar + 4 more
Histopathological image classification is an important task in medical image analysis. Recent approaches generally rely on weakly supervised learning due to the ease of acquiring case-level labels from pathology reports. However, patch-level classification is preferable in applications where only a limited number of…
Fabien Wehbe, Levi Adams, Samantha Yuen, Yoon-Seong Kim + 1 more
Application of single-cell/nucleus genomic sequencing to patient-derived tissues offers potential solutions to delineate disease mechanisms in human. However, individual cells in patient-derived tissues are in different pathological stages, and hence such cellular variability impedes subsequent differential gene…
Jie Zhao, Ying Gao, Chunjuan Bo, Dong Wang + 2 more
'Antonio Fernández-Caballero' 'Byung-Gyu Kim'] Visual object tracking is one of the core techniques in human-centered artificial intelligence, which is very useful for human-machine interaction. State-of-the-art tracking methods have shown their robustness and accuracy on many challenges. However, a large amount of…
Lei Zhou, Qinlin Xiao, Mohanmed Farag Taha, Chengjia Xu + 1 more
Deep learning and computer vision have become emerging tools for diseased plant phenotyping. Most previous studies focused on image-level disease classification. In this paper, pixel-level phenotypic feature (the distribution of spot) was analyzed by deep learning. Primarily, a diseased leaf dataset was collected and…
Ming Zhang, Bing Zeng, Sukho Lee, Dae-Ki Kang
Weakly supervised object detection (WSOD) has received increasing attention in object detection field, because it only requires image-level annotations to indicate the presence or absence of target objects, which greatly reduces the labeling costs. Existing methods usually focus on the current individual image to learn…
Yu-Feng Li, Ivor W. Tsang, James T. Kwok, Zhi‐Hua Zhou
In this paper, we study the problem of learning from weakly labeled data, where labels of the training examples are incomplete. This includes, for example, (i) semi-supervised learning where labels are partially known; (ii) multi-instance learning where labels are implicitly known; and (iii) clustering where labels are…
Jia-Rong Ou, Shu-Le Deng, Jin-Gang Yu, Loris Nanni
Weakly supervised instance segmentation (WSIS) provides a promising way to address instance segmentation in the absence of sufficient labeled data for training. Previous attempts on WSIS usually follow a proposal-based paradigm, critical to which is the proposal scoring strategy. These works mostly rely on certain…
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…
Sayedali Shetab Boushehri, Ahmad Bin Qasim, Dominik Waibel, Fabian Schmich + 1 more
Deep learning image classification algorithms typically require large annotated datasets. In contrast to real world images where labels are typically cheap and easy to get, biomedical applications require experts’ time for annotation, which is often expensive and scarce. Therefore, identifying methods to maximize…
Authors not listed
Recent years have seen a growing interest in machine learning approaches for chemical tasks. The best existing methods focus on building base models that combine molecular graphs (“2D structures”) with atomic coordinates in 3D to predict molecular properties, typically through pre-training followed by fine-tuning on…
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
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
David Buterez, Jon Paul Janet, Steven Kiddle, Pietro Liò
We investigate the potential of graph neural networks for transfer learning and improving molecular property prediction on sparse and expensive to acquire high-fidelity data by leveraging low-fidelity measurements as an inexpensive proxy for a targeted property ofinterest. This problem arises in discovery processes…
Paul Francoeur, Daniel Penaherrera, David Koes
The immense size of chemical space, the relative scarcity of high quality data, and the cost of running experiments to accurately measure molecular properties makes active learning (AL) an attractive approach to efficiently explore the space and train high-quality models for molecular property prediction. While AL is…
Hosein Fooladi, Steffen Hirte, Johannes Kirchmair
Today, machine learning methods are widely employed in drug discovery. However, the chronic lack of data continues to hamper their further development, validation, and application. Several modern strategies aim to mitigate the challenges associated with data scarcity by learning from data on related tasks. These…