Deep_Learning
-
DailyWildfireCAN: A Dataset and Benchmark for Next-Day Wildfire Spread Prediction in Canada
2026-11-10 15:29:13Mahacine Ettahri, Hager Radi Abdelwahed, Toluwaleke Olutayo, Daniel Aloise, Ga\'etan Marceau CaronAbstract
Wildfires represent a critical ecological and socio-economic challenge in Canada, where vast boreal forests and a warming climate have amplified the frequency and severity of extreme fire seasons. Predicting next-day wildfire spread can support fire management, but requires capturing complex interactions among weather, fuels, and terrain. Deep learning has emerged as a promising approach to learning these relationships from heterogeneous environmental data, yet its development and evaluation depend on representative datasets. Existing deep learning research on wildfire spread relies predominantly on US-focused datasets, leaving Canadian fire environments underrepresented. To address this gap, we present DailyWildfireCAN, a multi-modal, multi-temporal dataset and benchmark for predicting next-day wildfire spread across Canada. The dataset tracks continuous fire progression over multi-day events at a high spatial and temporal resolution of 90-m and 24-hour, respectively. This resolution preserves fine-scale information relevant to fire-edge geometry, local fuel variations, and terrain effects, supporting research toward improved daily wildfire spread forecasting. DailyWildfireCAN covers 2,440 wildfire events across eight fire seasons 2017-2024 and integrates 33 features across diverse modalities, including topography, meteorology, land cover, vegetation and forest structure, human influence indices, and satellite imagery. We establish a comprehensive benchmark comparing mono-temporal and multi-temporal prediction models, accompanied by a multi-dimensional evaluation framework assessing robustness across wildfire characteristics, geographic regions, and temporal conditions, spatial prediction quality, and sensitivity to reference label variations.
Abstract (translated)
URL
https://arxiv.org/abs/2611.14207
PDF
-
Obscured Wildfire Flame Detection by Spatio-temporal Analysis of Smoke Patterns Using Frame-wise Transformers
2026-11-10 13:37:13Uma Meleti, Abolfazl Razi, Fatemeh AfghahAbstract
In this paper, we propose a new paradigm in wildfire management for detecting both visible and obscured fire using Deep Learning (DL). The essence of our method is the joint spatial and temporal analysis of RGB videos, embracing the fact that temporal patterns of smoke motions in consecutive video frames can indicate obscured fire flames. This perspective contrasts the traditional approach of independently analyzing images and individual video frames. To this end, we propose a frame-wise transformer architecture that applies an attention mechanism between the consecutive frames while preserving spatial information within frames as opposed to regular attention mechanisms like the non-local block, which combines spatial and temporal information into a single vector. We also optimize the temporal pattern lengths under different spatial resolutions to achieve the highest performance while reducing the computational cost. To investigate the applicability of our method in aerial image processing, we applied it to a curated version of the FLAME2 dataset that includes dual-mode RGB/IR videos. The IR video feed is used to extract ground truth, while the model is trained only on RGB videos to maintain applicability to lowcost commercial drones (which are equipped only with regular cameras) and facilitate processing existing RGB-only datasets. Our proposed method achieves a Segmentation Foreground Dice score of 92.61%, an Object Detection Precision and Recall rate of 93.21% and 91.73%, and clip-level classification accuracy of 98.02%, respectively. We also investigated the efficacy and generalizability of our method through ablation study
Abstract (translated)
URL
https://arxiv.org/abs/2611.14074
PDF
-
CURRENT: A Recurrent Neural Network for Concurrent Spatial and Temporal Super-Resolution of 4D Flow MRI
2026-11-10 13:25:28Luuk Jacobs, Pietro Dirix, Sebastian KozerkeAbstract
Objective: To improve the spatial and temporal resolution of aortic 4D flow MRI through a unified deep learning framework for post-acquisition super-resolution (SR) of reconstructed velocity vector fields. Methods: We propose CURRENT, a recurrent neural network that performs spatial and temporal SR with a single network architecture. Supervised training was conducted using paired low- and high-resolution datasets generated by embedding computational fluid dynamics simulations of aortic flow into in vivo thoracic MRI backgrounds, followed by controlled spatial and temporal downsampling. CURRENT was evaluated for two-fold spatial and temporal SR against 4DFlowNet and for two-fold spatiotemporal SR against sequential spatial and temporal SR pipelines based on 4DFlowNet. Image-based and k-space-based quantitative metrics were considered and conventional interpolation were used as baselines. In addition, the impact of CURRENT's spatiotemporal SR on physics-informed relative pressure estimation was assessed. Finally, the ability of CURRENT to generalize from synthetic training data to in vivo acquisitions was investigated. Results: CURRENT achieved superior spatial SR performance and comparable temporal SR performance to dedicated 4DFlowNet models while providing a unified solution for both tasks. Simultaneous spatiotemporal SR using CURRENT yielded more accurate relative pressure estimates than either spatial or temporal SR alone and outperformed sequential 4DFlowNet-based spatial and temporal SR approaches. Furthermore, the proposed model demonstrated promising generalization to in vivo 4D flow MRI data despite being trained exclusively on synthetic datasets. Conclusion: CURRENT provides a unified framework for spatial and temporal SR of 4D flow MRI. The proposed approach has the potential to enhance physics-based analysis and broaden the applicability of accelerated 4D flow MRI.
Abstract (translated)
URL
https://arxiv.org/abs/2611.14058
PDF
-
Adapting a Vision Foundation Model for Fracture Characterization in Acoustic Image Logs
2026-11-10 11:04:27Guangyu Wang, Xiaodong Ma, Xinming WuAbstract
Natural fractures (NFs) and drilling-induced tensile fractures (DITFs) in acoustic image logs provide insights into rock mass structure and in situ stress, but manual characterization is time-consuming. Deep learning can automate this task, although expert-labeled data are scarce. We investigate whether adapting a pretrained vision foundation model (VFM) outperforms task-specific models trained from scratch with limited annotations. Using 80 annotated acoustic image samples, we fine-tuned DINOv3, pretrained on satellite imagery, and evaluated it on four boreholes excluded from fine-tuning. The adapted model estimated NF and DITF parameters comparably to human interpretation. Relative to U-Net, YOLOv8, and Swin Transformer models trained from scratch, it yielded fewer false positives and negatives and more accurate fracture counts and orientations. However, its performance advantage generally narrowed as training sample size decreased. We also assessed decoder architecture, fine-tuning strategy, and image enhancement. A progressive upsampling decoder with full fine-tuning performed best among the DINOv3 configurations tested, while fracture-focused image enhancement did not consistently improve accuracy. These results demonstrate the potential of VFM adaptation for fracture characterization in acoustic image logs with limited labeled data and suggest a transferable workflow for other annotation-scarce geoscientific imaging tasks.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13911
PDF
-
The BRIDGE dataset: paired brightfield whole-slide images of prostate core-needle biopsies in paraffin, after deparaffinization and H&E staining
2026-11-10 10:40:09Julia A. Mielcarz, Justyna Dur\'slewicz, Marek Zdrenka, Ahmad Abbadi, Martin Eklund, Mateusz Matusiak, {\L}ukasz Szylberg, Andrea CamilloniAbstract
Computational approaches are increasingly being explored to streamline histopathology workflows, including label-free methods that aim to reduce reliance on chemical staining. Among these, deep learning-based label-free virtual staining uses images of unstained tissue to computationally generate stained images. Despite growing interest, model development and validation remain constrained by the scarcity of paired, clinically annotated whole-slide imaging datasets. We present BRIDGE, a dataset of prostate core-needle biopsies collected at the Prof. Franciszek Lukaszczyk Oncology Centre in Bydgoszcz, Poland. The dataset comprises 869 glass slides from 169 patients, each digitized sequentially after sectioning, deparaffinization, and hematoxylin and eosin staining, yielding in total 2607 whole-slide images. The dataset supports the development and external validation of label-free virtual staining, virtual unstaining, image-registration and segmentation methods. Each slide is accompanied by a Gleason score, enabling downstream analyses of cancer classification and grading concordance. The stained images can be used independently for color normalization and cross-scanner robustness studies. All images are de-identified, distributed in their native scanner format and will be available through the BioImage Archive.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13883
PDF
-
Polarization Aware Deep Learning Framework for SAR Despeckling
2026-11-10 10:25:36Gianpaolo Passarello, Sergio Vitale, Hossein Aghababei, Giampaolo Ferraioli, Vito PascazioAbstract
Speckle is an inherent characteristic of synthetic aperture radar (SAR) imagery that complicates image interpretation and the extraction of reliable information. Although deep-learning-based despeckling methods have demonstrated promising performance, their applicability across different polarimetric configurations is often limited by the need for specialized architectures or separate training procedures. This paper presents a unified deep-learning framework for despeckling single-, dual-, and full-polarization SAR data using a single trained convolutional neural network. The proposed approach adopts a common nine-channel representation of the polarimetric covariance matrix and a polarization-aware masking strategy to accommodate different configurations without modifying the network architecture. Qualitative and quantitative comparison on three different scenes from different sensors demonstrate competitive performance across the considered experiments and support the feasibility of a unified, polarization-flexible approach to SAR despeckling across different polarimetric configurations.
Abstract (translated)
URL
https://arxiv.org/abs/2611.10091
PDF
-
picoEmergencyNet : Attention-Enhanced Ultra-Small Deep Learning Model for Aerial Image Classification in Emergency Monitoring
2026-11-10 06:28:44Christos KyrkouAbstract
Real-time disaster recognition from aerial imagery requires deep learning models that operate under strict memory, compute, and latency constraints. While recent approaches achieve high accuracy, their computational cost limits deployment on resource-constrained UAV platforms. This work introduces picoEmergencyNet, an ultra-lightweight convolutional neural network designed for aerial image disaster classification. The proposed architecture combines depthwise dilated convolutions with efficient channel attention and channel shuffling to enable multi-scale feature extraction at minimal computational cost. picoEmergencyNet has 23k parameters and 7M FLOPs, requiring only 0.1 MB of model storage. Evaluated on the AIDER and AIDERv2 disaster datasets, it achieves up to 96.1% accuracy, matching or exceeding state-of-the-art lightweight models that are orders of magnitude larger. Deployment experiments on the NVIDIA Jetson AGX Orin show that, without hardware-specific optimizations, picoEmergencyNet achieves 258 FPS on GPU and 46 FPS on CPU with a 7x lower activation memory footprint compared to competing models. These results demonstrate that careful architectural specialization, rather than model scaling, enables highly efficient and accurate TinyML-ready disaster recognition for real-time UAV-based emergency monitoring.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13638
PDF
-
Lightweight Deep Learning Framework for Astrocyte-Centric 3D Reconstruction across Normal and Stress-associated Ultrastructural States
2026-11-10 04:54:30Bowen Deng, Gwang-Jin Jeong, Seulgi Noh, Ji Young Mun, Na Young KimAbstract
The morphology of astrocytes is closely associated with their ability to support and modulate neural circuit functions; however, its reconstruction from volume electron microscopy (vEM) data remains labor-intensive and computationally resource-intensive, placing substantial barriers on its broader adoption and scalability despite the high spatial resolution of vEM. We present AstroSeg-vEM, a lightweight, astrocyte-centric framework for morphology-preserving astrocyte segmentation, 3D reconstruction, and quantitative morphological analysis in vEM images. AstroSeg-vEM combines transformer-based 2D segmentation with dedicated post-processing methods and operates directly on high-resolution EM sections, making it well suited to anisotropic vEM data and downstream proofreading workflows while preserving the full in-plane resolution of the original EM data. Across four mouse hippocampal datasets representing normal and morphologically defined stress-associated astrocyte ultrastructural states, AstroSeg-vEM demonstrates robust performance and generalization, achieving average Dice and weighted Dice values of 0.92 and 0.95, respectively, on a held-out test dataset. Its lightweight architecture enables efficient, high-fidelity reconstruction on consumer-grade hardware, providing a scalable and accessible platform for large-scale astrocyte ultrastructure analysis and future astrocyte-neuron interaction studies.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13581
PDF
-
Evidential Deep Learning with conditional entropy for glaucoma detection
Abstract
We propose a novel conditional entropy function as a basis for quantifying predictive uncertainty in Evidential Deep Learning (EDL) for glaucoma detection from retinal fundus images. Using the EyePACS-AIROGS-light-V2 and RIM-ONE-DL datasets for validation, we found that our proposed EDL model regularized by conditional entropy function produced uncertainty estimates with substantially lower calibration errors than Deep Ensembles, MC Dropout, post-hoc calibration and the conventional EDL framework based on Dempster uncertainty. To further probe our models' interpretability, we analyzed Grad-CAM heatmaps using a focus index representing spatial concentration. Compared with the other uncertainty quantification methods, EDL models exhibited greater separation between the index distributions of correctly and incorrectly classified fundus images. In particular, false negatives were associated with localized Grad-CAM activations that were displaced from the optic disc, consistent with its established clinical importance in glaucoma assessment. Overall, our findings suggest that our proposed EDL framework for glaucoma detection provides not only enhanced uncertainty calibration but also admits a more physiologically consistent interpretability.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13513
PDF
-
Rethinking Evaluation for Video Capsule Endoscopy: Validation Protocol, Source-Video Diversity, and Cross-Dataset Shift
2026-11-09 21:20:54Smriti Regmi, Debesh JhaAbstract
Automated video capsule endoscopy (VCE) classification remains challenging due to severe class imbalance, limited source-video diversity, and variations in image appearance across datasets. Although deep learning models have demonstrated promising performance on established benchmarks, their reliability under different validation protocols and external dataset shift remains insufficiently understood. In this work, we introduce DMSNet, a deformable adaptive multi-scale edge-guided attention network integrating adaptive multi-scale feature fusion, deformable lesion refinement, edge-guided attention, and sparse feature selection. We benchmark DMSNet against 18 representative convolutional, transformer-based, and pretrained vision architectures on Kvasir-Capsule under random frame-level and source-video-disjoint validation protocols. We further identify source-video overlap between the original training and test splits and evaluate model performance on both the complete test set and a source-video-disjoint subset. Under random frame-level validation, DMSNet achieves an MCC of 0.442 $\pm$ 0.020 on complete test set and 0.456 $\pm$ 0.037 on the source-video-disjoint subset. On the external CV2024 dataset, ConvNeXt-Tiny achieves the highest MCC of 0.207, while performance degrades substantially across architectures relative to in-domain evaluation. We further investigate source-video diversity using a per-class cap of 200 training frames. With the total training-set size held fixed, increasing the maximum source-video coverage from four to eight improves MCC for 17 of 19 evaluated architectures. Overall, these findings demonstrate that model rankings and classification performance depend substantially on evaluation protocol, training-source diversity, and cross-dataset shift, emphasizing the need for source-aware validation and rigorous external evaluation of VCE classification systems.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13245
PDF
-
Comparative Evaluation of Deep Learning Models for Geographic Atrophy Segmentation in En Face OCT Images
2026-11-09 20:12:13Utkarsh Doshi, Adarsh Gadari, Sharat Chandra Vupparaboina, Joseph DeCicco, Giulia Gregori, Nicola Valsecchi, Nasiq Hasan, Elham Sadeghi, Jos\`e-Alain Sahel, Jay Chhablani, Kiran Kumar Vupparaboina, Sandeep Chandra BollepalliAbstract
Background: Geographic atrophy (GA) is an advanced form of age-related macular degeneration (AMD). It causes irreversible vision loss due to progressive degeneration of the retinal pigment epithelium (RPE) and photoreceptors. Accurate segmentation of GA in en-face image is crucial for early diagnosis, monitoring disease progression, and developing personalized treatment strategies. However, existing deep learning approaches are typically evaluated using single architectures and single-device datasets, limiting insight into relative performance and cross-device robustness. Methods: We trained and systematically compared five deep learning architectures for GA segmentation: Nested U-Net, ResU-Net, nnUnet, Swin-Unet and MedSegDiff model, together with a thresholding based classical baseline. The models were trained on a dataset of en-face OCT images derived from 269 OCT volumes from two different devices, following a three-step preprocessing technique involving normalization and inpainting to enhance the quality of the en-face images. Results: On the fixed held-out validation set comprising OCT scans from both Cirrus (R) (Carl Zeiss Meditec, Inc., USA) and Spectralis (R) (Heidelberg Engineering, Germany) devices, nnUnet achieved the highest mean Dice similarity coefficient (DSC) of 0.8882 +/- 0.0033. The other models yielded the following Dice scores: Nested U-Net (0.7932 +/- 0.0423), ResU-Net (0.8453 +/- 0.0074), Swin-Unet (0.6886 +/- 0.0294), and Diffusion model (0.7959 +/- 0.0242). Conclusions: nnUnet framework, demonstrated most reliable performance for GA segmentation in en-face OCT under limited data conditions. It offers the best balance of accuracy, stability, and efficiency for GA quantification.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13191
PDF
-
Structured Visual Landscape: Generating Preferred Representations in Biological and Artificial Neural Networks
2026-11-06 05:00:38Yusong Zhao, Edgar Y. Walker, Hao Wang, Mi LuAbstract
Understanding how neurons respond to visual stimulus is an important question in both deep learning and neuroscience. It has significant implications for enhancing the interpretability of black-box artificial neural networks and for understanding the visual representation in the human brain. We propose a structured visual representations landscape and design an activation-based prior that allows us to effectively regularize the landscape with activations from brain regions or neural network units. Our model Vis-Lens integrates a variational autoencoder and a diffusion model into an image generation framework. It enables the generation of natural, realistic preferred images by directly modifying the activation-regularized latent codes without per-image iterative optimization. We demonstrate the effectiveness of our framework on both artificial neural networks and human visual cortex using functional magnetic resonance imaging (fMRI) data. Our framework improves activation modulation and image realism compared with a recent state-of-the-art method for generating preferred visual representations of these neural targets. Code is available at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2611.03597
PDF
-
Federated training performs as well as centralized for 3D Latent Diffusion for Diagnosis-Conditioned Brain MRI Synthesis
2026-11-05 22:35:29Abhijith Shaji, Paul Kurian, Greg Ver Steeg, Paul M. Thompson, Jose-Luis AmbiteAbstract
Generative models of brain MRI are useful to understand brain conditions, and to enrich scarce and imbalanced neuroimaging data. However, training these models requires large, multi-site training sets, which is challenging. Federated learning (FL) allows for training deep learning models without sharing data. We show that training a 3D conditional latent model federatedly (where data is kept private at each site) performs similarly to centralized training (where all the data have been copied to a single site). We evaluate three metrics: conditional fidelity (how well generated MRIs perform in classification), diversity (how the generated images match the real data distribution, using MultiScale-SSIM), and realism (how close generated images are to real images, using Fréchet Inception Distance). Our results show that federated training is a viable route to develop multi-site generative models of brain MRI.
Abstract (translated)
URL
https://arxiv.org/abs/2611.08771
PDF
-
An Anatomy-guided 3D Segmentation Pipeline for Prostate Surgical Phantoms from CT Scans
2026-11-05 19:58:47Iven Chen, Kuan-I Chung, Ayberk Acar, Susheela Sharma Stern, Alejandro O. Chara, Jie Ying Wu, Daniel MoyerAbstract
Full autonomy has been a long-term goal in robotic surgery and a near-to-medium-term goal for prostatectomy. Due to the relatively closed structure of the procedure, its standard-of-care robotic/laparoscopic solutions, and procedure economics, prostate procedures are an optimal early target for surgical automation. Prototyping and testing of possible solutions is done in constructed laboratory phantoms, either synthetic tissues or animal phantoms. To enable measurement of perceptual and robotic motion error, phantoms undergo volumetric imaging; these images are critical to both a potential deployed automatic system, used in planning motion and treatment (cutting, ablation, suturing, etc.), as well as post-hoc assessment of system performance. Rapid iteration on these phantom models yields small, inconsistent datasets, while the current workflow for human annotation is often anatomically unreliable and prohibitively time-consuming, rendering deep learning unsuitable at reasonable scales. To address these limitations, we present an automated 3D segmentation pipeline that uses classical medical image-processing techniques to encode the known anatomy of the phantoms as explicit geometric and intensity priors, which requires no training corpus and is transferred to each new phantom revision without re-annotation. Across 57 scans spanning multiple phantom revisions, our pipeline recovers the capsule interior at 0.83 median Dice on 11 contrast-suppressed scans and detects all 32 annotated focal lesions without false positives, completing a full scan in 2-8 minutes on commodity CPU hardware versus 1-2 hours by hand.
Abstract (translated)
URL
https://arxiv.org/abs/2611.08652
PDF
-
OraRefer: Segmentation-Guided Multi-Photograph Fusion for Oral Cancer Screening with Guaranteed Patient-Level Sensitivity
2026-11-05 17:39:50Rupam Mukherjee, Mehakdeep Singh, Shirin Dasgupta, Subhamoy MandalAbstract
Oral cancer and the oral potentially malignant disorders (OPMD) that can precede it are visible on the oral mucosa, which makes smartphone photographs a promising tool for screening. Deep learning models for this task classify individual photographs. However, screening refers patients, not photographs: a patient is usually photographed several times, and the referral decision must miss no more than a set share of patients with disease. We propose OraRefer, a patient-level screening model that reads each photograph with a segmentation-guided foundation model that conditions its class decision on the predicted lesion, combines all photographs of a patient with risk factors, and refers through a threshold with a finite-sample sensitivity guarantee. The model localizes the mouth itself, so inference needs no annotation. On 3000 photographs of 714 patients, patient-level fusion reaches an AUROC of 0.919 and outperforms attention-based multiple-instance learning. At a 90% sensitivity target, OraRefer detects 95% of patients with OPMD or cancer and misses 3 of 61 patients with cancer, at a specificity of 0.610. Because the threshold is calibrated without retraining, each clinic can set its own guaranteed operating point on a local calibration sample.
Abstract (translated)
URL
https://arxiv.org/abs/2611.08360
PDF
-
LLM-Assisted Label Noise Correction for EEG-Based Sleep Stage Classification
2026-11-05 17:12:05Elmira Salari, Shruti KshirsagarAbstract
Automatic sleep stage classification from electroencephalography (EEG) signals is a critical task in clinical sleep medicine. While deep learning models have shown promising results in this domain, their performance is fundamentally constrained by the quality of training labels, which human experts manually annotate and that are subject to inter-rater disagreement and systematic labeling errors. In this paper, we propose a novel LLM-assisted label-noise filtering pipeline that operates exclusively during training to identify and remove unreliable sleep-stage annotations before retraining the classifier. Specifically, TinySleepNet is first trained on the original dataset to extract per-epoch SoftMax confidence scores, then low-confidence epochs are submitted to LLaMA 3.1 along with their EEG spectral features and surrounding sequence context. The LLM evaluates each epoch against AASM sleep-scoring guidelines and assigns a verdict of RELIABLE or SUSPICIOUS. Suspicious epochs are removed from the training set, and TinySleepNet is retrained on the cleaned data. Evaluated on the Sleep-EDF Expanded dataset using 20-fold cross-validation, our method improves overall accuracy from 81.8% to 84.6% and Macro F1-score from 75.6% to 77.4%, with particularly notable gains in REM detection. Crucially, because the LLM operates only during training, the lightweight TinySleepNet architecture (1.3M parameters) is preserved at inference, making the proposed pipeline suitable for real-time, wearable sleep monitoring applications.
Abstract (translated)
URL
https://arxiv.org/abs/2611.08311
PDF
-
DynSHAP: Towards Explainable Dynamic Survival Analysis
2026-11-05 12:11:57Nastasya Anokhina, Jonas J\"ur{\ss}, Pietro Li\`oAbstract
Deep learning models for dynamic survival analysis (DSA) achieve strong predictive performance by incorporating longitudinal patient data, but their black box nature limits clinical trust and adoption. Existing explainability methods cannot handle longitudinal, irregular inputs and functional survival outputs simultaneously, which limits their usability in DSA. We propose DynSHAP, a SHAP framework suited specifically for dynamic survival analysis. It extends common marginal SHAP estimators to this setting by treating time--feature pairs as players in the Shapley game. We further introduce Temporal DynSHAP, which learns linear dependencies in features over time and uses conditional sampling to address them in explanations. When applied to synthetic data with known ground-truth attributions, Temporal DynSHAP recovers temporally dependent features more accurately than marginal estimators for a given state-of-the-art model. Applied to two real-world clinical datasets and two DSA architectures, DynSHAP produces attributions faithful to model learning, allowing medical experts to see which patient information drove the prediction and when.
Abstract (translated)
URL
https://arxiv.org/abs/2609.13042
PDF
-
Enhanced MRI brain tumor detection using deep learning in conjunction with explainable AI SHAP based diverse and multi feature analysis
2026-11-05 11:20:24Asif Rahman, Maqsood Hayat, Nadeem Iqbal, Fawaz Khaled Alarfaj, Salem Alkhalaf, Fahad AlturiseAbstract
Recent innovations in medical imaging have markedly improved brain tumor identification, surpassing conventional diagnostic approaches that suffer from low resolution, radiation exposure, and limited contrast. Magnetic Resonance Imaging (MRI) is pivotal in precise and accurate tumor characterization owing to its high-resolution, non-invasive nature. This study investigates the synergy among multiple feature representation schemes such as local Binary Patterns (LBP), Gabor filters, Discrete Wavelet Transform, Fast Fourier Transform, Convolutional Neural Networks (CNN), and Gray-Level Run Length Matrix alongside five learning algorithms namely: k-nearest Neighbor, Random Forest, Support Vector Classifier (SVC), and probabilistic neural network (PNN), and CNN. Empirical findings indicate that LBP in conjunction with SVC and CNN obtained high specificity and accuracy, rendering it a promising method for MRI-based tumor diagnosis. Further to investigate the contribution of LBP, Statistical analysis chi-square and p-value tests are used to confirm the significant impact of LBP feature space for identification of brain Tumor. In addition, The SHAP analysis was used to identify the most important features in classification. In a small dataset, CNN obtained 97.8% accuracy while SVC yielded 98.06% accuracy. In subsequent analysis, a large benchmark dataset is also utilized to evaluate the performance of learning algorithms in order to investigate the generalization power of the proposed model. CNN achieves the highest accuracy of 98.9%, followed by SVC at 96.7%. These results highlight CNN's effectiveness in automated, high-precision tumor diagnosis. Code: this https URL
Abstract (translated)
URL
https://arxiv.org/abs/2611.07843
PDF
-
Equivariant Deep Learning in Direct-Product Space for Electronic-Structure Hamiltonian Prediction
2026-11-05 09:19:07Shi Yin, Zujian Dai, Xinyang Pan, Lixin HeAbstract
Most existing equivariant architectures for Hamiltonian prediction propagate features in direct-sum spaces of irreducible spherical tensors, whereas the target Hamiltonian is naturally an operator block in a direct-product representation over local orbital and spin bases. This representation mismatch fragments contiguous Hamiltonian blocks, obscures direct manipulation of orbital couplings, and organizes features into irregular angular-momentum components that require numerous transformations and are poorly aligned with efficient hardware execution and memory management. To address both the representation mismatch and computational bottleneck, we propose DP-Learner, an equivariant learning framework that performs message passing directly on Hamiltonian-like operator blocks in the direct-product space. Theoretically, we propose Lie-generator-based directional embedding and invariantly weighted left--right products and contractions that preserve equivariance to SO(3), SU(2), and spatial inversions, without explicit Clebsch--Gordan decomposition or inverse reconstruction. Methodologically, we instantiate this principle as a Transformer architecture, in which Hamiltonian-like feature matrices or tensors evolve through equivariant operators in the direct-product space, while non-linear invariant scalar routing controls structured gating over matrix units, directional propagation, attention, and environment-embedded context interactions. We further construct MP-HAM-110K, a large-scale Hamiltonian dataset comprising more than 110,000 material structures covering 68 elements with both Hamiltonian and wavefunction data. Experiments on MP-HAM-110K and Materials-HAM-SOC demonstrate that DP-Learner dramatically improves Hamiltonian prediction accuracy and inference efficiency over the state-of-the-art method.
Abstract (translated)
URL
https://arxiv.org/abs/2611.07695
PDF
-
Robust Ranking of Port Impacts from Impending Tropical Cyclones: A Two-Stage Recommendation System
2026-11-05 01:19:53Chenchen Kuai, Shixuan Tang, Chaolun Ma, Xiaoqiang Kong, Xiubin Bruce Wang, Yunlong Zhang, Zihao LiAbstract
When a tropical cyclone (TC) threatens several ports, preparedness planning depends on anticipating which ports are likely to be affected and where disruptions may last longest. Limited historical observations at individual ports and uncertainty in landfall location make these relative impacts difficult to estimate. We use a two-stage recommendation framework that first selects candidate ports based on their proximity to the expected landfall location and then ranks them by their estimated number of impact days. Using AIS-derived impact days for 37 ports and 31 TCs in the Gulf of Mexico study region, we compare three ways of representing port--cyclone interactions: pairwise feature interactions, tree-based feature combinations, and explicit feature crosses combined with learned embeddings. In tests on Hurricanes Nicole and Ian, the retrieval stage consistently identifies all observed affected ports under landfall uncertainty. Among the ranking models, Wide & Deep Learning achieves the closest agreement with the observed port rankings. Its mean ranking agreement exceeds that of the distance-based baseline by 85.5% for Nicole and 55.5% for Ian and is less sensitive to uncertainty in the landfall location. These rankings could help port authorities prioritize post-cyclone inspections and allocate recovery resources across ports threatened by the same event.
Abstract (translated)
URL
https://arxiv.org/abs/2611.07296
PDF
