#deep learning
82 resultsComparing the Performance of Foundation Model Derived Embeddings with Traditional Approaches for Distant Metastasis Prediction in Head and Neck Cancer
Erich Schmitz, Meixu Chen, Bowen Jing +1
The study evaluates CT foundation model embeddings for predicting distant metastasis in head and neck cancer and finds they outperform traditional radiomics and deep‑learning featu…
Formal Verification for Deep Learning-based Power Control in Massive MIMO
Thanh Le, Takeshi Matsumura, Yusheng Ji +1
The paper introduces a formal verification framework that uses abstract interpretation (DeepPoly) to certify the robustness of deep‑learning based power allocation in multi‑cell ma…
Prospective clinical indication, post-hoc report leakage, and fusion design in multi-image chest radiograph classification: a patient-clustered evaluation
Kamran Shahid, Muhammad Munwar Iqbal
The paper evaluates how different multimodal fusion strategies that combine chest X‑ray images with clinical indication text affect disease classification performance, and quantifi…
DINE: Distance Is Not Enough -- Learning Global Deformation Priors for Robust Soft-Tissue Point Cloud Registration
Sara Monji-Azad, Rohit Beer, Marvin Kinz +2
The paper introduces DINE, a framework that improves non-rigid soft-tissue point cloud registration by combining distance-based objectives with a learned global prior on deformatio…
Converting T1-weighted MRI from 3T to 7T quality using deep learning
Malo Gicquel, Ruoyi Zhao, Anika Wuestefeld +12
The paper introduces deep learning models (a U‑Net and a GAN‑enhanced U‑Net) that generate synthetic 7 T‑quality T1‑weighted brain MRI from standard 3 T scans, achieving image deta…
Toward Energy-Efficient and Low-Power Arrhythmia Detection for Wearable Devices
Floriaan Bulten, Yawar Rasheed, Arlene John +2
The paper proposes using reduced data precision and approximate multipliers in a deep‑learning model to lower the power consumption of wearable arrhythmia detectors while keeping h…
Gibbs randomness-compression proposition
M. Süzen, M Süzen
The paper proposes a theorem linking Gibbs entropy (a measure of randomness) to lossy model compression, showing that the entropy of remaining network weights correlates with learn…
Selectivity Drives Efficiency: Dataset Pruning for Visual Place Recognition
Tong Jin, Yunpeng Liu, Shuyu Hu +3
The paper introduces a place-wise dataset pruning method for visual place recognition that selects informative locations using intra-place diversity and inter-place similarity metr…
Automated identification of Ichneumonoidea wasps via YOLO-based deep learning: Integrating HiresCam for Explainable AI
Joao Manoel Herrera Pinheiro, Gabriela Do Nascimento Herrera, Alvaro Doria Dos Santos +7
The paper presents a YOLO-based deep learning system combined with HiResCAM to automatically identify Ichneumonoidea wasp families from high‑resolution images, achieving over 96% a…
Causal-Adversarial Probing of Clinical Covariates for Prostate MRI Grading
Yipei Wang, Shiqi Huang, Wen Yan +6
The paper introduces an adversarial causal‑reasoning framework to identify which clinical covariates help or hinder deep‑learning models for prostate MRI cancer grading, showing th…
Robust Betatron-Tune Measurement from Schottky Spectra: Complementary Classical and Deep-Learning Paradigms
Peihan Sun, Manzhou Zhang, Renxian Yuan +2
The paper presents two methods—a classical matched‑filter approach and a deep‑learning CNN with Bayesian tracking—to measure betatron tune from noisy Schottky spectra in medical pr…
PUe: Biased Positive-Unlabeled Learning Enhancement by Causal Inference
Xutao Wang, Hanting Chen, Tianyu Guo +1
The paper proposes PUe, a framework that improves positive‑unlabeled (PU) learning under biased label selection by using normalized propensity scores and inverse probability weight…
Star-forming clump detection in nearby galaxies using Faster R-CNN and $ugrizy$ imaging data from CLAUDS and HSC-SSP
Jürgen J. Popp, Hugh Dickinson, Stephen Serjeant +4
The paper presents a deep‑learning object detection pipeline based on Faster R‑CNN and a Zoobot backbone to locate giant star‑forming clumps in low‑redshift galaxies using six‑band…
What Does Goodness Measure? A Likelihood-Ratio Account of Forward-Forward Learning
Paolo Giannitrapani
The paper explains that the Forward-Forward algorithm’s goodness measure is actually a likelihood‑ratio statistic under a generative model, and shows how different data distributio…
Comparison of Dimension Reduction Methods for EEG Seizure Detection Using Autonomous AI-Driven Optimization
Annika Stiehl, Vishal Kagade, Nicolas Weeger +3
The paper compares four dimension‑reduction techniques for multichannel EEG seizure detection and uses an autonomous AI framework to jointly optimize the representation and deep‑le…
Uniform Approximation of Functions with Asymmetric Growth and Decay by Deep Weighted Polynomials
Kingsley Yeon, Steven B. Damelin
The paper proposes a class of weighted deep (composite) polynomials that can uniformly approximate functions which grow on one side of the real line and decay on the other, and dem…
Neural Architectures for Amortized Bayesian Inference: Statistical Foundations and Empirical Assessments
Roy Shivam Ram Shreshtth, Arnab Hazra, Gourab Mukherjee
The paper examines how neural network architectures such as feedforward nets, Deep Sets, and Transformers can be used to amortize Bayesian inference, providing fast approximate pos…
EEG-based AI-BCI Wheelchair Advancement: Transformer-Based Learning with Motor Imagery for Brain Computer Interface
Bipul Thapa, Biplov Paneru, Bishwash Paneru +1
The paper proposes a Transformer‑based deep learning model (TFormerEEG) to classify motor‑imagery EEG signals for controlling a simulated wheelchair, achieving over 90% accuracy.
Enhancing WISE Infrared Imaging to Spitzer Resolution Using Deep Learning Super-Resolution
Saeed Rezaee, Shoubaneh Hemmati, Bahram Mobasher +4
The paper introduces a deep‑learning model that upscales WISE infrared images to near‑Spitzer resolution, achieving about 4.6× higher spatial detail and improved flux recovery and…
Evaluation of State-of-the-Art Deep Learning Architectures for Aerodynamical Predictions
Jan Scherz, Derrick Hines, Philipp Bekemeyer
The paper benchmarks four modern deep learning operator learning models as surrogate solvers for aerodynamic predictions, evaluating their ability to predict surface pressure on 2D…
Multimodal Assessment of Pancreatic Cancer Resectability Using Deep Learning
Vincent Ochs, Christoph Kuemmerli, Florentin Bieder +12
The paper introduces a fully automated deep learning system that combines 3D contrast‑enhanced CT scans with routine clinical variables to classify pancreatic cancer patients into…
Video to All-in-focus Image Reconstruction Algorithm for Automated Microscopic Urinalysis
Chinmay Nema, Hari Om Aggrawal, Dipam Goswami +2
The paper presents a method that records a short video while manually adjusting focus and reconstructs an all‑in‑focus image from the frames, enabling automated deep‑learning based…
Predicting BESS Degradation with Uncertainty Quantification: A Probabilistic Framework for Battery Energy Storage Systems
Melina Graner, Holger Hesse, Andreas Jossen
The paper presents a deep‑learning based probabilistic framework that predicts battery state‑of‑health and quantifies uncertainty, scaling from cell‑level data to whole‑system degr…
Deep learning of committor and explainable artificial intelligence analysis for identifying reaction coordinates
Toshifumi Mori, Kei-ichi Okazaki, Kang Kim +1
The paper presents a framework that uses deep neural networks to learn the committor function for identifying reaction coordinates in complex molecular systems, and applies explain…