A searchable index of real negative results, null findings, and replication failures from the published literature — so you can learn what didn't work before repeating it.
WASTE indexes published research — it does not host or republish full papers. Each entry is a metadata record (title, authors, DOI) compiled from open scholarly databases, with the abstract shown in full only where the paper is openly licensed (e.g. Creative Commons); otherwise a short excerpt is shown for reference under fair use. WASTE classifies each work by failure type; classifications are automated and approximate.
81 results for "deep learning" · page 1 of 3
Jiawei Fan, Jiazhou Wang, Zhi Chen et al. · 2018 · Medical Physics
Purpose To develop an automated treatment planning strategy for external beam intensity‐modulated radiation therapy ( IMRT ), including a deep learning‐based three‐dimensional (3D) dose prediction and a dose distribution‐based plan…
Marc-André Schulz, B.T. Thomas Yeo, Joshua T Vogelstein et al. · 2020 · Nature Communications
Recently, deep learning has unlocked unprecedented success in various domains, especially using images, text, and speech. However, deep learning is only beneficial if the data have nonlinear relationships and if they are exploitable at available sample sizes. We systematically profiled the performance of deep, kernel, and linear models as a function of sample size on UKBiobank brain images against established machine learning references. On MNIST and Zalando Fashion, prediction accuracy consistently improves when escalating from linear models to shallow-nonlinear models, and further improves w
Frederick M. Howard, James M. Dolezal, Sara Kochanny et al. · 2021 · Nature Communications
The Cancer Genome Atlas (TCGA) is one of the largest biorepositories of digital histology. Deep learning (DL) models have been trained on TCGA to predict numerous features directly from histology, including survival, gene expression patterns, and driver mutations. However, we demonstrate that these features vary substantially across tissue submitting sites in TCGA for over 3,000 patients with six cancer subtypes. Additionally, we show that histologic image differences between submitting sites can easily be identified with DL. Site detection remains possible despite commonly used color normaliz
Michel E. Vandenberghe, Marietta Scott, Paul W. Scorer et al. · 2017 · Scientific Reports
Tissue biomarker scoring by pathologists is central to defining the appropriate therapy for patients with cancer. Yet, inter-pathologist variability in the interpretation of ambiguous cases can affect diagnostic accuracy. Modern artificial intelligence methods such as deep learning have the potential to supplement pathologist expertise to ensure constant diagnostic accuracy. We developed a computational approach based on deep learning that automatically scores HER2, a biomarker that defines patient eligibility for anti-HER2 targeted therapies in breast cancer. In a cohort of 71 breast tumour r
Hyunwoo Yang, Eun Jo, Hyung Jun Kim et al. · 2020 · Journal of Clinical Medicine
Patients with odontogenic cysts and tumors may have to undergo serious surgery unless the lesion is properly detected at the early stage. The purpose of this study is to evaluate the diagnostic performance of the real-time object detecting deep convolutional neural network You Only Look Once (YOLO) v2-a deep learning algorithm that can both detect and classify an object at the same time-on panoramic radiographs. In this study, 1602 lesions on panoramic radiographs taken from 2010 to 2019 at Yonsei University Dental Hospital were selected as a database. Images were classified and labeled into f
Xuhan Liu, Kai Ye, Herman van Vlijmen et al. · 2023 · Journal of Cheminformatics
Abstract Rational drug design often starts from specific scaffolds to which side chains/substituents are added or modified due to the large drug-like chemical space available to search for novel drug-like molecules. With the rapid growth of deep learning in drug discovery, a variety of effective approaches have been developed for de novo drug design. In previous work we proposed a method named DrugEx , which can be applied in polypharmacology based on multi-objective deep reinforcement learning. However, the previous version is trained under fixed objectives and does not allow users to input a
Mohammed Saqib, Ying Sha, May D. Wang · 2018
Sepsis is a life-threatening condition caused by infection and subsequent overreaction by the immune system. Physicians effectively treat sepsis with early administration of antibiotics. However, excessive use of antibiotics on false…
Shuming Jiao, Yang Gao, Jun Feng et al. · 2020 · Optics Express
Deep learning has been extensively applied in many optical imaging problems in recent years. Despite the success, the limitations and drawbacks of deep learning in optical imaging have been seldom investigated. In this work, we show that…
Matthew R. Masters, Amr H. Mahmoud, Markus A. Lill · 2025 · Nature Communications
Co-folding models represent a major innovation in deep-learning-based protein-ligand structure prediction. The recent publications of RoseTTAFold All-Atom, AlphaFold3, and others have shown high-quality results on predicting the structures of proteins interacting with small-molecules, nucleic-acids, and other proteins. Despite these advanced capabilities and broad potential, the current study presents critical findings that question the adherence of these models to fundamental physical principles. Through adversarial examples based on established physical, chemical, and biological principles,
Matias Lindgren · 2020 · Aaltodoc (Aalto University)
This thesis applies deep learning based classification techniques to identify natural languages from speech. The primary motivation behind this thesis is to implement accurate techniques for segmenting multimedia materials by the languages…
Marco Roccetti, Giovanni Delnevo, Luca Casini et al. · 2021 · Journal Of Big Data
Abstract Deep learning models are tools for data analysis suitable for approximating (non-linear) relationships among variables for the best prediction of an outcome. While these models can be used to answer many important questions, their utility is still harshly criticized, being extremely challenging to identify which data descriptors are the most adequate to represent a given specific phenomenon of interest. With a recent experience in the development of a deep learning model designed to detect failures in mechanical water meter devices, we have learnt that a sensible deterioration of the
Osval A. Montesinos‐López, J. Cricelio Montesinos-López, P. K. Singh et al. · 2020 · G3 Genes Genomes Genetics
The paradigm called genomic selection (GS) is a revolutionary way of developing new plants and animals. This is a predictive methodology, since it uses learning methods to perform its task. Unfortunately, there is no universal model that can be used for all types of predictions; for this reason, specific methodologies are required for each type of output (response variables). Since there is a lack of efficient methodologies for multivariate count data outcomes, in this paper, a multivariate Poisson deep neural network (MPDN) model is proposed for the genomic prediction of various count outcome
Uğur Demirel, Handan Çam, Ramazan Ünlü · 2020 · GAZI UNIVERSITY JOURNAL OF SCIENCE
Stock market prediction in financial and commodity markets is a major challenge for speculators, investors, and companies but also profitable with an accurate prediction. Thus, obtaining accurate prediction results becomes extremely…
Matthias Langer, Ashley Hall, Zhen He et al. · 2018 · IEEE Transactions on Parallel and Distributed Systems
Many distributed deep learning systems have been published over the past few years, often accompanied by impressive performance claims. In practice these figures are often achieved in high performance computing (HPC) environments with fast…
Marwa Radwan, Abdelhameed Ibrahim, M. A. Abdelsalam et al. · 2026 · Scientific Reports
Deep learning models often encounter two key challenges in developing intelligent and scalable forecasting frameworks for renewable energy systems: input feature space dimensionality and sensitivity to hyperparameter settings. These limitations increase computational cost and compromise generalization and robustness. This paper presents a hybrid deep learning-optimization framework that leverages cognitively inspired metaheuristics to address these challenges, employing the Binary iHow Optimization Algorithm (biHOW) for feature selection and its continuous counterpart, iHOW, for hyperparameter
Tong He, Ru Kong, Avram J. Holmes et al. · 2018
In recent years, deep learning has transformed the field of machine learning. In the field of neuroimaging, there are increasing interests in adopting deep learning techniques. However, deep neural networks (DNNs) usually need large…
Seunghyeon Wang · 2026 · Scientific Reports
Ensuring consistent compliance with Personal Protective Equipment (PPE) requirements on construction sites is crucial for worker safety. Although deep learning-based methods already perform well in detecting non-PPE cases, there is still…
Kyle Lesinger, Di Tian · 2025 · Nature Communications
Deep neural networks that learn from climate reanalysis data have produced skillful weather forecasts within ten days. However, it is still a great challenge for dynamic models to predict soil moisture, droughts, and other extreme events…
Maureen van Eijnatten, Leonardo Rundo, Batenburg, Joost et al. · 2021 · Data Archiving and Networked Services (DANS)
Background and Objectives: Deep learning is being increasingly used for deformable image registration and unsupervised approaches, in particular, have shown great potential. However, the registration of abdominopelvic Computed Tomography…
Daniel M. Goldenholz, Celena Eccleston, Robert Moss et al. · 2024 · Epilepsia
OBJECTIVE: Recently, a deep learning artificial intelligence (AI) model forecasted seizure risk using retrospective seizure diaries with higher accuracy than random forecasts. The present study sought to prospectively evaluate the same…
Aras Fahrettin Korkmaz, Fatih Ekinci, Şehmus Altaş et al. · 2025 · Biology
This study presents a novel approach for classifying Discomycetes species using deep learning and explainable artificial intelligence (XAI) techniques. The EfficientNet-B0 model achieved the highest performance, reaching 97% accuracy, a 97% F1-score, and a 99% AUC, making it the most effective model. MobileNetV3-L followed closely, with 96% accuracy, a 96% F1-score, and a 99% AUC, while ShuffleNet also showed strong results, reaching 95% accuracy and a 95% F1-score. In contrast, the EfficientNet-B4 model exhibited lower performance, achieving 89% accuracy, an 89% F1-score, and a 93% AUC. These
Youngjin Yoo, Pascal Ceccaldi, Siqi Liu et al. · 2021 · Journal of Medical Imaging
Purpose: We investigate the impact of various deep-learning-based methods for detecting and segmenting metastases with different lesion volume sizes on 3D brain MR images. Approach: A 2.5D U-Net and a 3D U-Net were selected. We also…
Rasit Dinc, Nurittin Ardıç · 2026 · Cells
Exosomes and other extracellular vesicles (EVs) carry microRNAs, proteins, and lipids that reflect cardiovascular pathophysiology and can enable minimally invasive biomarker discovery. However, EV datasets are highly dimensional and heterogeneous, strongly influenced by pre-analytic variables and non-standardized isolation/characterization workflows, limiting reproducibility across studies. Artificial intelligence (AI), including machine learning (ML), deep learning (DL), and network-based approaches, can support EV biomarker development by integrating multi-omics profiles with clinical metada
Remita Austin, R. Parvathi · 2025 · Scientific Reports
The absence of reliable early treatment serves as one of the main causes of cervical cancer. Hence, it is crucial to detect cervical cancer early. The biggest challenge in diagnosing cervical cancer early is that it is asymptomatic until it develops into invasive carcinoma. In medical applications, the use of machine learning and deep learning is successful as a classifier in the preliminary identification of cancerous cells in the cervical region. In our study, we present a CNN-based method for the classification of cervical cancer cells. We present a method for accurately classifying Pap sme
Farhan Sheth, Ishika Chatter, Manvendra Jasra et al. · 2025 · Plant Methods
Herbs have historically been central to medicinal practices, representing one of the earliest forms of therapeutic intervention. While synthetic drugs are often highly effective in treating acute conditions, their use is frequently…
Şafak Kılıç · 2025 · PLoS ONE
BACKGROUND AND OBJECTIVE: Male fertility assessment through sperm morphology analysis remains a critical component of reproductive health evaluation, as abnormal sperm morphology is strongly correlated with reduced fertility rates and poor assisted reproductive technology outcomes. Traditional manual analysis performed by embryologists is time-intensive, subjective, and prone to significant inter-observer variability, with studies reporting up to 40% disagreement between expert evaluators. This research presents a novel deep learning framework combining Convolutional Block Attention Module (CB
Van Nguyen, Sreenidhi Iyengar, Haroon Rasheed et al. · 2025 · Ophthalmology Science
Purpose Develop and test a deep learning (DL) algorithm for detecting referable glaucoma. Design Retrospective cohort study. Participants A total of 6116 patients from the Los Angeles County (LAC) Department of Health Services (DHS) were…
Lihua Liang, Xiaodong Li, Shutong Liu et al. · 2026 · Buildings
This study develops and applies an integrated methodology that combines deep learning-based computer vision and spatial statistics to automate the large-scale identification and analysis of morphological features in vernacular courtyard dwellings. Focusing on Liangshuaixiu dwellings in Wu’an, southern Hebei, we trained an HRNetV2 semantic segmentation model on high-resolution satellite imagery to identify and extract contours for 134,280 courtyard spaces. Core morphological parameters (area, orientation) were calculated and analyzed using GIS spatial statistics and the geographic detector mode
Mateja Napravnik, Franko Hržić, Martin Urschler et al. · 2025 · Scientific Reports
Deep learning models require large amounts of annotated data, which are hard to obtain in the medical field, as the annotation process is laborious and depends on expert knowledge. This data scarcity hinders a model's ability to generalise…
Rafael Cabral, Ricardo Santos, José A.F.O. Correia et al. · 2025 · Sensors
The automated inspection of civil infrastructure with Unmanned Aerial Vehicles (UAVs) is hampered by the challenge of accurately segmenting multi-damage in high-resolution imagery. While foundational models like the Segment Anything Model (SAM) offer data-efficient segmentation, their effectiveness is constrained by prompting strategies, especially for geometrically complex defects. This paper presents a comprehensive comparative analysis of deep learning strategies to identify an optimal deep learning pipeline for segmenting cracks, efflorescences, and exposed rebars. It systematically evalua