CoSWA-YOLOv12 is a compact YOLOv12 instance‑segmentation detector designed to improve detection of tiny malaria parasites in microscopy images. It introduces a Cooperative Scale‑adaptive Wasserstein Assignment that applies a Wasserstein distance‑based label assignment inversely proportional to object size, a wavelet detail residual, and a min‑max Gaussian regression loss, all of which preserve pretrained weights. On a five‑class Rwandan thick‑smear dataset, the method raises P. falciparum recall from 0.63 to 0.74, increases mAP@50 from 0.73 to 0.81, and reduces missed detections from 38% to 15%.
arXiv:2607. 00385v1 Announce Type: cross Abstract: Automated malaria diagnosis from blood smear microscopy is a critical challenge in global health AI; in resource-limited settings, the scarcity of expert microscopists remains the primary bottleneck to timely and accurate diagnosis.
arXiv:2607. 00385v2 Announce Type: replace-cross Abstract: Automated malaria diagnosis from blood smear microscopy is a critical global health AI challenge; expert scarcity remains the primary diagnostic bottleneck.
arXiv:2608. 08566v1 Announce Type: cross Abstract: Malaria remains a leading cause of mortality in resource-limited settings, where expert microscopists are scarce.
EMFE (Efficient Mathematical Feature Extraction) is a lightweight, explainable machine‑learning framework that classifies single red‑blood‑cell images as parasitized or uninfected using five engineered features: Gray World color normalization, adaptive green‑channel thresholding, morphological spot detection, and classical classifiers. On the NIH LHNCBC malaria dataset (27,558 images from 200 patients), a tuned Random Forest achieved 94.6% pooled out‑of‑fold accuracy, 94.3% on a 40‑patient holdout, and outperformed deep‑learning baselines in an accuracy‑efficiency trade‑off. Ablation studies, synthetic perturbations, and explainability analyses identified spot saturation as the dominant discriminative feature and quantified the framework’s failure modes and patient‑level performance.
The study examined whether white blood cells (WBCs) cause false‑positive malaria detections in Giemsa‑stained blood smears. Two YOLOv12 models—one trained only on parasite labels and another on both parasite and WBC labels—were evaluated on 8,000 images from Uganda and Ghana. Across seven spatial and statistical tests, false positives did not cluster near WBCs; instead, most were background detections, and the multi‑task model (with WBC labels) performed better overall, suggesting that WBC labeling alone does not reduce false positives but that stain artifacts and unannotated ring forms are the true sources of error.
arXiv:2606. 00092v1 Announce Type: cross Abstract: Weakly-supervised classification of whole-slide images with attention-based multiple instance learning (ABMIL) on top of foundation features now reaches near-saturation on Camelyon16 slide-level performance, but the corresponding attention maps are an imperfect localization signal: in clinical interpretation, a model that classifies correctly without firing on the actual lesion is hard to trust.
arXiv:2607. 16324v1 Announce Type: cross Abstract: Malaria diagnosis in endemic regions depends on species-level identification of Plasmodium parasites in thick blood smears, but deep learning detectors classify detections without providing morphological evidence for their predictions, limiting the ability of microscopists to audit those predictions at the case level.
AdaptiveCDM is a modular framework for source‑free few‑shot domain adaptation in cell detection, enabling a pretrained model to adapt to new imaging domains using only a handful of labeled target images and no source data. It combines Resolution‑Aware Augmentation (RAug) to balance scarce, class‑imbalanced samples while preserving cellular morphology, and Category‑Aware Representation Learning (CARL) to strengthen class‑consistent proposals for better localization and classification. Experiments on M5 and Raabin‑WBC datasets show that AdaptiveCDM achieves competitive or superior mAP scores compared to state‑of‑the‑art methods under their respective supervision settings.
Background-Free Objectness Learning (B-FOR) is a dense, class‑agnostic detection framework that learns objectness without treating unlabeled regions as background. It predicts multi‑scale object‑center and scale fields, using spatially structured soft targets to supervise only reliable annotated areas and introduces displacement‑aware scale fields to model object extent. Experiments on PASCAL VOC, MS‑COCO, and Open Images show B‑FOR improves recall by over +10 AR points compared to prior class‑agnostic baselines, with ablation studies confirming the importance of localized supervision and displacement‑aware scaling.
arXiv:2606. 00844v1 Announce Type: cross Abstract: Bounding-box regression is a fundamental component of object detection, playing a critical role in precise object localization.
arXiv:2609.26549v1 Announce Type: new Abstract: Individual tree crown segmentation from aerial imagery underpins tree-level carbon accounting, biodiversity, and restoration monitoring at landscape sc...
arXiv:2607. 14703v1 Announce Type: cross Abstract: Multiple instance learning (MIL) has become the main paradigm for whole-slide image (WSI) analysis in computational pathology.