Decoupling Parcellation from Classification: Systematic Benchmark of Fast Brain Segmentation Methods for Alzheimer's Disease Detection

• URL: http://arxiv.org/abs/2608.16039v1
• Date: Mon, 17 Aug 2026 03:08:29 GMT
• Title: Decoupling Parcellation from Classification: Systematic Benchmark of Fast Brain Segmentation Methods for Alzheimer's Disease Detection
• Abstract summary: We benchmark fast deep learning parcellation methods against the FreeSurfer (FS-HV) clinical baseline.<n>Our factorial design evaluates three parcellation methods, two volumetry strategies, and four classifier paradigms.
• Score: 22.040630187626693
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Brain parcellation and classification are typically evaluated in isolation, yet downstream AD detection performance depends on their interaction. We decouple these components and systematically benchmark fast deep learning parcellation methods (SynthSeg+, OpenMAP-T1) against the FreeSurfer (FS-HV) clinical baseline through down- stream AD classification on OASIS-1. Our factorial design evaluates three parcellation methods, two volumetry strategies (hard vs. soft), and four classifier paradigms (clinical thresholds, supervised feedforward networks, ensemble methods, and foundation models with zero/few-shot prompting), with all results quantified using BCa Bootstrap 95% confidence intervals.

Related papers

• GOLDMARK: Governed Outcome-Linked Diagnostic Model Assessment Reference Kit [7.891604152731754]
  Computational biomarkers (CBs) are histo-derived patterns extracted from hematoxylin-eosin (H&E) whole-slide images (WSIs) to predict therapeutic response or prognosis.<n>We introduce GOLDMARK, a standardized benchmarking framework built on a curated TCGA cohort with clinically actionable OncoKB level 1-3 biomarker labels.
  arXiv  Detail & Related papers  (2026-03-21T15:09:06Z)
• TCG CREST System Description for the DISPLACE-M Challenge [11.144212535817674]
  This report presents the TCG CREST system description for Track 1 (Speaker Diarization) of the DISPLACE-M challenge.<n>Our study evaluates the impact of various voice activity detection (VAD) methods and advanced clustering algorithms on speaker diarization (SD) performance.
  arXiv  Detail & Related papers  (2026-03-02T16:12:47Z)
• LPD: Learnable Prototypes with Diversity Regularization for Weakly Supervised Histopathology Segmentation [17.25487101903999]
  Weakly supervised semantic segmentation (WSSS) in histopathology is hindered by inter-class homogeneity, intra-class heterogeneity, and CAM-induced region shrinkage.<n>We propose a cluster-free, one-stage learnable-prototype framework with diversity regularization to enhance morphological intra-class heterogeneity coverage.<n>Our approach achieves state-of-the-art (SOTA) performance on BCSS-WSSS, outperforming prior methods in mIoU and mDice.
  arXiv  Detail & Related papers  (2025-12-05T17:59:16Z)
• Hierarchical Self-Supervised Representation Learning for Depression Detection from Speech [51.14752758616364]
  Speech-based depression detection (SDD) is a promising, non-invasive alternative to traditional clinical assessments.<n>We propose HAREN-CTC, a novel architecture that integrates multi-layer SSL features using cross-attention within a multitask learning framework.<n>The model achieves state-of-the-art macro F1-scores of 0.81 on DAIC-WOZ and 0.82 on MODMA, outperforming prior methods across both evaluation scenarios.
  arXiv  Detail & Related papers  (2025-10-05T09:32:12Z)
• Benchmarking Dimensionality Reduction Techniques for Spatial Transcriptomics [0.0]
  We introduce a unified framework for evaluating dimensionality reduction techniques in spatial transcriptomics.<n>We benchmark six methods PCA, NMF, autoencoder, VAE, and two hybrid embeddings on a cholangiocarcinoma Xenium dataset.
  arXiv  Detail & Related papers  (2025-09-12T17:27:34Z)
• AHDMIL: Asymmetric Hierarchical Distillation Multi-Instance Learning for Fast and Accurate Whole-Slide Image Classification [51.525891360380285]
  AHDMIL is an Asymmetric Hierarchical Distillation Multi-Instance Learning framework.<n>It eliminates irrelevant patches through a two-step training process.<n>It consistently outperforms previous state-of-the-art methods in both classification performance and inference speed.
  arXiv  Detail & Related papers  (2025-08-07T07:47:16Z)
• LadderMIL: Multiple Instance Learning with Coarse-to-Fine Self-Distillation [18.92114069747481]
  Multiple Instance Learning (MIL) for whole slide image (WSI) analysis in computational pathology often neglects instance-level learning.<n>LadderMIL is a framework designed to improve MIL through two perspectives: (1) employing instance-level supervision and (2) learning inter-instance contextual information at bag level.
  arXiv  Detail & Related papers  (2025-02-04T20:41:02Z)
• Learning to Perform Downlink Channel Estimation in Massive MIMO Systems [72.76968022465469]
  We study downlink (DL) channel estimation in a Massive multiple-input multiple-output (MIMO) system. A common approach is to use the mean value as the estimate, motivated by channel hardening. We propose two novel estimation methods.
  arXiv  Detail & Related papers  (2021-09-06T13:42:32Z)
• Visualizing Classifier Adjacency Relations: A Case Study in Speaker Verification and Voice Anti-Spoofing [72.4445825335561]
  We propose a simple method to derive 2D representation from detection scores produced by an arbitrary set of binary classifiers. Based upon rank correlations, our method facilitates a visual comparison of classifiers with arbitrary scores. While the approach is fully versatile and can be applied to any detection task, we demonstrate the method using scores produced by automatic speaker verification and voice anti-spoofing systems.
  arXiv  Detail & Related papers  (2021-06-11T13:03:33Z)
• Does Your Dermatology Classifier Know What It Doesn't Know? Detecting the Long-Tail of Unseen Conditions [18.351120611713586]
  We develop and rigorously evaluate a deep learning based system that can accurately classify skin conditions. We frame this task as an out-of-distribution (OOD) detection problem. Our novel approach, hierarchical outlier detection (HOD) assigns multiple abstention classes for each training class and jointly performs a coarse classification of inliers vs. outliers.
  arXiv  Detail & Related papers  (2021-04-08T15:15:22Z)
• Capturing scattered discriminative information using a deep architecture in acoustic scene classification [49.86640645460706]
  In this study, we investigate various methods to capture discriminative information and simultaneously mitigate the overfitting problem. We adopt a max feature map method to replace conventional non-linear activations in a deep neural network. Two data augment methods and two deep architecture modules are further explored to reduce overfitting and sustain the system's discriminative power.
  arXiv  Detail & Related papers  (2020-07-09T08:32:06Z)
• LSD-C: Linearly Separable Deep Clusters [145.89790963544314]
  We present LSD-C, a novel method to identify clusters in an unlabeled dataset. Our method draws inspiration from recent semi-supervised learning practice and proposes to combine our clustering algorithm with self-supervised pretraining and strong data augmentation. We show that our approach significantly outperforms competitors on popular public image benchmarks including CIFAR 10/100, STL 10 and MNIST, as well as the document classification dataset Reuters 10K.
  arXiv  Detail & Related papers  (2020-06-17T17:58:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.