arXiv subjects

Soujanya Hazra

Publications and source records attributed to Soujanya Hazra.

5 recordsLinked to original sources

arXiv2026

Graph-Guided Structured Recovery of Modulo EEG Signals

Electroencephalography (EEG) acquisition is often affected by large-amplitude variations across subjects and sessions. This makes conventional front ends vulnerable to saturation and clipping. Modulo sampling addresses this limitation by folding the signal into a bounded range rather than truncating it. The recovery task is then to reconstruct the original waveform from periodic observations, which is a highly ill-posed inverse problem. In this work, we propose a compact signal-structured framework for modulo EEG recovery. The method combines phase-aware features, graph-temporal modeling, and structured fold-state decoding. A boundary-guided gating technique improves inference near wrap events, where uncertainty is highest. Experiments across two EEG datasets, over a wide range of folding thresholds, demonstrate consistent recovery performance. Ablation and noise studies further support the contribution of the structured decoding technique. The proposed signal-processing approach effectively recovers folded EEG recordings. These results show that structured fold-state decoding is effective for recovering folded EEG. The code is accessible at https://github.com/soujo/graphunwrapnet.

eess.SP↗

arXiv2026

Uncertainty-Aware Multimodal Fusion for Oral Lesion Classification

Early detection of oral cancer and potentially malignant diseases is a major challenge in low-resource settings due to the scarcity of annotated data. We provide a unified approach for oral lesion classification that incorporates deep learning, spectral analysis, and demographic data. A pathologist verified subset of oral cavity images was curated from a publicly available dataset. Oral cavity pictures were processed using a fine tuned ConvNeXtv2 network for deep embeddings before being translated into the hyperspectral domain using a reconstruction algorithm. Haemoglobin sensitive, textural, and spectral descriptors were obtained from the reconstructed hyperspectral cubes and combined with demographic data. Multiple machine learning models were evaluated using patient specific validation. Finally, an incremental heuristic meta learner (IHML) was developed that merged calibrated base classifiers via probabilistic feature stacking and uncertainty-aware abstraction of multimodal representations with patient level smoothing. By decoupling evidence extraction from decision fusion, IHML stabilizes predictions in heterogeneous, small sample medical datasets. On an unseen test set, our proposed model achieved a macro F1 of 66.23% and an overall accuracy of 64.56%. The findings demonstrate that RGB to hyperspectral reconstruction and ensemble meta learning improve diagnostic robustness in real world oral lesion screening.

eess.IV↗

arXiv2025

RAA-MIL: A Novel Framework for Classification of Oral Cytology

Cytology is a valuable tool for early detection of oral squamous cell carcinoma (OSCC). However, manual examination of cytology whole slide images (WSIs) is slow, subjective, and depends heavily on expert pathologists. To address this, we introduce the first weakly supervised deep learning framework for patient-level diagnosis of oral cytology whole slide images, leveraging the newly released Oral Cytology Dataset [1], which provides annotated cytology WSIs from ten medical centres across India. Each patient case is represented as a bag of cytology patches and assigned a diagnosis label (Healthy, Benign, Oral Potentially Malignant Disorders (OPMD), OSCC) by an in-house expert pathologist. These patient-level weak labels form a new extension to the dataset. We evaluate a baseline multiple-instance learning (MIL) model and a proposed Region-Affinity Attention MIL (RAA-MIL) that models spatial relationships between regions within each slide. The RAA-MIL achieves an average accuracy of 72.7%, weighted F1-score of 0.69 on an unseen test set, outperforming the baseline. This study establishes the first patient-level weakly supervised benchmark for oral cytology and moves toward reliable AI-assisted digital pathology.

eess.IV↗

arXiv2025

Selection and Stability of Functional Connectivity Features for Classification of Brain Disorders

Brain disorders are an umbrella term for a group of neurological and psychiatric conditions that have a major effect on thinking, feeling, and acting. These conditions encompass a wide range of conditions. The illnesses in question pose significant difficulties not only for individuals, but also for healthcare systems all across the world. In this study, we explore the capability of explainable machine learning for classification of people who suffer from brain disorders. This is accomplished by the utilization of brain connection map, also referred as connectome, derived from functional magnetic resonance imaging (fMRI) data. In order to analyze features that are based on the connectome, we investigated several different feature selection procedures. These strategies included the Least Absolute Shrinkage and Selection Operator (LASSO), Relief, and Analysis of Variance (ANOVA), in addition to a logistic regression (LR) classifier. First and foremost, the purpose was to evaluate and contrast the classification accuracy of different feature selection methods in terms of distinguishing healthy controls from diseased individuals. The evaluation of the stability of the traits that were chosen was the second objective. The identification of the regions of the brain that have an effect on the classification was the third main objective. When applied to the UCLA dataset, the LASSO approach, which is our most effective strategy, produced a classification accuracy of 91.85% and a stability index of 0.74, which is greater than the results obtained by other approaches: Relief and ANOVA. These methods are effective in locating trustworthy biomarkers, which adds to the development of connectome-based classification in the context of issues that impact the brain.

q-bio.NC↗

arXiv2025

Bridging Accuracy and Explainability in EEG-based Graph Attention Network for Depression Detection

Depression is a major cause of global mental illness and significantly influences suicide rates. Timely and accurate diagnosis is essential for effective intervention. Electroencephalography (EEG) provides a non-invasive and accessible method for examining cerebral activity and identifying disease-associated patterns. We propose a novel graph-based deep learning framework, named Edge-gated, axis-mixed Pooling Attention Network (ExPANet), for differentiating major depressive disorder (MDD) patients from healthy controls (HC). EEG recordings undergo preprocessing to eliminate artifacts and are segmented into short periods of activity. We extract 14 features from each segment, which include time, frequency, fractal, and complexity domains. Electrodes are represented as nodes, whereas edges are determined by the phase-locking value (PLV) to represent functional connectivity. The generated brain graphs are examined utilizing an adapted graph attention network. This architecture acquires both localized electrode characteristics and comprehensive functional connectivity patterns. The proposed framework attains superior performance relative to current EEG-based approaches across two different datasets. A fundamental advantage of our methodology is its explainability. We evaluated the significance of features, channels, and edges, in addition to intrinsic attention weights. These studies highlight features, cerebral areas, and connectivity associations that are especially relevant to MDD, many of which correspond with clinical data. Our findings demonstrate a reliable and transparent method for EEG-based screening of MDD, using deep learning with clinically relevant results.

eess.SP↗