arXiv Computer Vision
Few-Shot Cross-Dataset Adaptation for Tuberculosis Detection Using DenseNet
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arXiv AI
Jul 13
TCLA: Training-Free Class-wise Logit Adaptation for Medical Vision-Language Models
arXiv:2607. 09562v1 Announce Type: cross Abstract: Medical Vision-Language Models (VLMs) exhibit strong zero-shot performance, yet their effectiveness still declines on out-of-distribution (OOD) data due to domain shifts and class bias inherited from large-scale pretraining.
arXiv Machine Learning
Aug 27
Why ML-based cough models do not generalize: a systematic cross-dataset evaluation for tuberculosis screening
The study investigates why machine‑learning models for tuberculosis screening based on cough acoustics fail to generalize across datasets. Classical ML and deep‑learning classifiers achieved moderate performance within their own datasets (ROC‑AUC up to 0.755) but performed poorly on external data, often below 0.6. The authors found that audio features were more influenced by recording device and dataset than by TB status, and that device‑diverse training improved transfer while device mismatch degraded it. A clinical‑variable baseline showed more consistent generalization, suggesting acquisition‑specific variability is a stronger driver of poor generalizability than population shift.
arXiv Computer Vision
4d ago
Semi-Supervised Domain Adaptation with Latent Diffusion for Pathology Image Classification
arXiv:2601.17228v2 Announce Type: replace Abstract: Deep learning models in computational pathology often fail to generalize across cohorts and institutions due to domain shift. Existing approaches e...
arXiv Machine Learning
Jul 21
DA-MergeLoRA: Hypernetwork-Based LoRA Merging for Few-Shot Test-Time Domain Adaptation
arXiv:2607. 17467v1 Announce Type: cross Abstract: Few-shot Test-Time Domain Adaptation (FSTT-DA) seeks to adapt models to novel domains using only a handful of unlabeled target samples.
arXiv Computer Vision
Aug 26
Example-based Robust Abnormality Detection with Minimal Annotations using Exemplar Med-DETR
arXiv:2608.24281v1 Announce Type: new Abstract: Reducing annotation requirements remains a key challenge in developing robust medical object detectors. To address this, Vision-Language (VL) object de...
arXiv AI
Sep 1
ActiveAugment: Online Active Learning for Augmentation Selection in Deep Learning
arXiv:2608.28923v1 Announce Type: cross Abstract: Data augmentation is a cornerstone of deep learning pipelines, yet existing strategies treat it as a static, model-agnostic preprocessing step, eithe...