arXiv subjects

Anwar P. P. Abdul Majeed

Publications and source records attributed to Anwar P. P. Abdul Majeed.

3 recordsLinked to original sources

arXiv2026

SegTME-UNI2: A Foundation Model-Based Framework for Generalisable Multiclass Cell Segmentation and LLM-Driven Tumour Microenvironment Characterisation in Histopathology

Characterising the TME from routine H&E-stained histology images requires simultaneous cell segmentation, biological feature extraction, and interpretable clinical reporting. We present SegTME-UNI2, a unified framework addressing all three requirements end-to-end: a segmentation backbone that converts raw H\&E patches into per-nucleus class labels, a structured feature-extraction pipeline that turns those labels into quantitative TME descriptors, and a language-model narrative generator that turns those descriptors into clinician-readable text. At its core is UNI2-UperHoVer, a dual-head multiscale segmentation model that pairs UNI2 with two parallel UperNet decoders: one for six-class semantic segmentation and one for HV gradient regression enabling watershed-based nuclear instance separation. It is trained via a three-stage progressive pseudo-label curriculum, scaling from PanNuke (Stage 1, 0.25um/pixel) to TCGA-UT Scale-0 (Stage 2, 0.5um/pixel) and full 1.6M-patch, six-scale TCGA-UT (Stage 3, 0.5 to 1.0um/pixel). TCGA-UT's coarser, broader per-patch context than PanNuke's also permits a larger tile stride during whole-slide inference. This pipeline computes 22 per-patch compositional, morphological, spatial-entropy, and intercellular-distance metrics and translates them into six categorical phenotype labels and a standardised biological-token vocabulary, fine-tuned via NVIDIA BioNeMo that converts into clinically grounded narratives whose individual claims can be spot-checked directly against the underlying features. Qualitative validation on IGNITE NSCLC tiles shows the pipeline produces biologically coherent phenotype classifications and narratives despite inter-institutional stain variability and imperfect segmentation. The pseudo-labelled TCGA-UT dataset and UNI2-UperHoVer checkpoints are publicly released to support large-scale TME profiling and spatial biology research.

cs.CV↗

arXiv2026

From Flat Facts to Sharp Hallucinations: Detecting Stubborn Errors via Gradient Sensitivity

Traditional hallucination detection fails on "Stubborn Hallucinations" - errors where LLMs are confidently wrong. We propose a geometric solution: Embedding-Perturbed Gradient Sensitivity (EPGS). We hypothesize that while robust facts reside in flat minima, stubborn hallucinations sit in sharp minima, supported by brittle memorization. EPGS detects this sharpness by perturbing input embeddings with Gaussian noise and measuring the resulting spike in gradient magnitude. This acts as an efficient proxy for the Hessian spectrum, differentiating stable knowledge from unstable memorization. Our experiments show that EPGS significantly outperforms entropy-based and representation-based baselines, providing a robust signal for detecting high-confidence factual errors.

cs.LG↗

arXiv2026

GroupKAN: Efficient Kolmogorov-Arnold Networks via Grouped Spline Modeling

Medical image segmentation demands models that achieve high accuracy while maintaining computational efficiency and clinical interpretability. While recent Kolmogorov-Arnold Networks (KANs) offer powerful adaptive non-linearities, their full-channel spline transformations incur a quadratic parameter growth of $\mathcal{O}(C^{2}(G+k))$ with respect to the channel dimension $C$, where $G$ and $k$ denote the number of grid intervals and spline polynomial order, respectively. Moreover, unconstrained spline mappings lack structural constraints, leading to excessive functional freedom, which may cause overfitting under limited medical annotations. To address these challenges, we propose GroupKAN (Grouped Kolmogorov-Arnold Networks), an efficient architecture driven by group-structured spline modeling. Specifically, we introduce: (1) Grouped KAN Transform (GKT), which restricts spline interactions to intra-group channel mappings across $g$ groups, effectively reducing the spline-induced quadratic expansion to \textbf{$\mathcal{O}(C^2(\frac{G+k}{g} + 1))$}, thereby significantly lowering the effective quadratic coefficient; and (2) Grouped KAN Activation (GKA), which applies shared spline functions within each group to enable efficient token-wise non-linearities. By imposing structured constraints on channel interactions, GroupKAN achieves a substantial reduction in parameter redundancy without sacrificing expressive capacity.Extensive evaluations on three medical benchmarks (BUSI, GlaS, and CVC) demonstrate that GroupKAN achieves an average IoU of 79.80\%, outperforming the strong U-KAN baseline by +1.11\% while requiring only 47.6\% of the parameters (3.02M vs. 6.35M). Qualitative results further reveal that GroupKAN produces sharply localized activation maps that better align with the ground truth than MLPs and KANs, significantly enhancing clinical interpretability.

cs.CV↗