30 papers · ranked by Valyu relevance
Shih-Cheng Huang, Anuj Pareek, Malte Jensen, Matthew P. Lungren + 2 more
'Serena Yeung' 'Akshay S. Chaudhari'] Advancements in deep learning and computer vision provide promising solutions for medical image analysis, potentially improving healthcare and patient outcomes. However, the prevailing paradigm of training deep learning models requires large quantities of labeled training data…
Saeed Shurrab, Rehab Duwairi, Jiachen Yang
The scarcity of high-quality annotated medical imaging datasets is a major problem that collides with machine learning applications in the field of medical imaging analysis and impedes its advancement. Self-supervised learning is a recent training paradigm that enables learning robust representations without the need…
Thomas E. Tavolara, Metin N. Gurcan, M. Khalid Khan Niazi, Ognjen Arandjelović
'Ognjen Arandjelović'] Simple Summary Recent AI methods in the automated analysis of histopathological imaging data associated with cancer have trended towards less supervision by humans. Yet, there are circumstances when humans cannot lend a hand to AI. Hence, we present an unsupervised method to learn meaningful…
Longlong Jing, Yingli Tian
—Large-scale labeled data are generally required to train deep neural networks in order to obtain better performance in visual feature learning from images or videos for computer vision applications. To avoid extensive cost of collecting and annotating large-scale datasets, as a subset of unsupervised learning methods…
Asaki Kataoka, Yoshihiro Nagano, Masafumi Oizumi
Recent advances in self-supervised learning have attracted significant attention from both machine learning and neuroscience. This is primarily because self-supervised methods do not require annotated supervisory information, making them applicable to training artificial networks without relying on large amounts of…
Niall Rodgers
Palaeontology has seen widespread and growing use of machine learning to classify and analyse large datasets of fossils. However, palaeontology is a challenging field in which to apply machine learning. Datasets may be small or unlabelled, images may be complex and different from standard datasets and palaeontologists…
Blake VanBerlo, Jesse Hoey, Alexander Wong
Self-supervised pretraining has been observed to be effective at improving feature representations for transfer learning, leveraging large amounts of unlabelled data. This review summarizes recent research into its usage in X-ray, computed tomography, magnetic resonance, and ultrasound imaging, concentrating on studies…
Markus Marks, Manuel Knott, Neehar Kondapaneni, Elijah Cole + 3 more
'Thijs Defraeye' 'Fernando Perez-Cruz' 'Pietro Perona'] Self-supervised learning (SSL) is a machine learning approach where the data itself provides supervision, eliminating the need for external labels. The model is forced to learn about the data’s inherent structure or context by solving a pretext task. With SSL…
Gabriel Díaz, Billy Peralta, Luis Caro, Orietta Nicolis + 1 more
'Gastone C. Castellani'] Automatic recognition of visual objects using a deep learning approach has been successfully applied to multiple areas. However, deep learning techniques require a large amount of labeled data, which is usually expensive to obtain. An alternative is to use semi-supervised models, such as…
Utku Özbulak, Hyun‐Jung Lee, Beril Boga, Esla Timothy Anzaku + 4 more
'Ho-min Park' 'Arnout Van Messem' 'Wesley De Neve' 'Joris Vankerschaver'] Although supervised learning has been highly successful in improving the state-of-the-art in the domain of image-based computer vision in the past, the margin of improvement has diminished significantly in recent years, indicating that a plateau…
Bihi Sabiri, Amal Khtira, Bouchra El Asri, Maryem Rhanoui + 2 more
'Raimondo Schettini' 'Donald Bailey'] In recent years, contrastive learning has been a highly favored method for self-supervised representation learning, which significantly improves the unsupervised training of deep image models. Self-supervised learning is a subset of unsupervised learning in which the learning…
Hong Liu, Jeff Z. HaoChen, Adrien Gaidon, Tengyu Ma
Self-supervised learning (SSL) is a scalable way to learn general visual representations since it learns without labels. However, large-scale unlabeled datasets in the wild often have long-tailed label distributions, where we know little about the behavior of SSL. In this work, we systematically investigate…
Attaullah Sahito, Eibe Frank, Bernhard Pfahringer
> Abstract. Self-training is a simple semi-supervised learning approach: Unlabelled examples that attract high-confidence predictions are labelled with their predictions and added to the training set, with this process being repeated multiple times. Recently, self-supervision—learning without manual supervision by…
Steven M. Peterson, Rajesh P. N. Rao, Bingni W. Brunton
Recent advances in neural decoding have accelerated the development of brain-computer interfaces aimed at assisting users with everyday tasks such as speaking, walking, and manipulating objects. However, current approaches for training neural decoders commonly require large quantities of labeled data, which can be…
Samuel Albanie, Erika Lu, João F. Henriques
In the quiet backwaters of cs.CV, cs.LG and stat.ML, a cornucopia of new learning systems is emerging from a primordial soup of mathematics—learning systems with no need for external supervision. To date, little thought has been given to how these self-supervised learners have sprung into being or the principles that…
Philip Toma, Olga Ovcharenko, Imant Daunhawer, Julia Vogt + 2 more
Self-supervised learning (SSL) has emerged as a powerful approach for learning biologically meaningful representations of single-cell data. To establish best practices in this domain, we present a comprehensive benchmark evaluating eight SSL methods across three downstream tasks and eight datasets, with various data…
E. Moebel, C. Kervrann
Cryo electron tomography visualizes native cells at nanometer resolution, but analysis is challenged by noise and artifacts. Recently, supervised deep learning methods have been applied to decipher the 3D spatial distribution of macromolecules. However, in order to discover unknown objects, unsupervised classification…
Hilal AlQuabeh, Ameera Bawazeer, Abdulateef Alhashmi
Data labeling in supervised learning is considered an expensive and infeasible tool in some conditions. The self-supervised learning method is proposed to tackle the learning effectiveness with fewer labeled data, however, there is a lack of confidence in the size of labeled data needed to achieve adequate results.…
Mélanie Lubrano, Tristan Lazard, Guillaume Balezo, Yaëlle Bellahsen-Harrar + 3 more
In computational pathology, predictive models from Whole Slide Images (WSI) mostly rely on Multiple Instance Learning (MIL), where the WSI are represented as a bag of tiles, each of which is encoded by a Neural Network (NN). Slide-level predictions are then achieved by building models on the agglomeration of these tile…
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…
Xinnan Du, William Zhang, Jose M. Álvarez
— Deep learning perception models require a massive amount of labeled training data to achieve good performance. While unlabeled data is easy to acquire, the cost of labeling is prohibitive and could create a tremendous burden on companies or individuals. Recently, self-supervision has emerged as an alternative to…
Álex Hernández-García
The renaissance of artificial neural networks was catalysed by the success of classification models, tagged by the community with the broader term supervised learning. The extraordinary results gave rise to a hype loaded with ambitious promises and overstatements. Soon the community realised that the success owed much…
Authors not listed
Machine learning approaches for conceptualizing and designing in silico compounds have attracted significant attention. However, the applicability of these compounds is often challenged by synthetic viability and cost-effectiveness. Researchers introduced proxy-scores, known as synthethic accessiblity scoring, to…
Rahil Gholamipoor, Nima Rafiee, Markus Kollmann
X-ray images have been widely used for medical diagnoses of cardiothoracic and pulmonary abnormalities due to its noninvasiveness. Advancement in computer-aided diagnostic technologies, such as deep supervised methods, can help radiologists with a reliable early treatment and reduce diagnosis time. Nevertheless, these…
Hongyang Dong, Simon D.M. Jacques, Keith T. Butler, Olof Gutowski + 4 more
In this study, we introduce a method designed to eliminate parallax artefacts present in Xray powder diffraction computed tomography data acquired from large samples. These parallax artefacts manifest as artificial peak shifting, broadening and splitting, leading to inaccurate physicochemical information, such as…
Ross Irwin, Spyridon Dimitriadis, Jiazhen He, Esben Bjerrum
Transformer models coupled with Simplified Molecular Line Entry System (SMILES) have recently proven to be a powerful combination for solving challenges in cheminformatics. These models, however, are often developed specifically for a single application and can be very resource-intensive to train. In this work we…
Authors not listed
Machine learning models are increasingly applied to heterogeneous materials datasets spanning different synthesis routes, measurement protocols, and structural classes. Although multi-task and representation-learning approaches are commonly used to improve predictive performance, the latent representations learned by…
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…
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…
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…