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.
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.
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.
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.
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:2609.09417v1 Announce Type: new Abstract: Vision-language models (VLMs) show promise for agricultural classification, but zero-shot performance on disease, pest, damage, quality, and species id...
arXiv:2607. 21155v1 Announce Type: cross Abstract: Knowledge-Intensive Visual Question Answering (KI-VQA) benchmarks evaluate Vision-Language Models (VLMs) as multimodal knowledge assistants by requiring external information beyond a provided image to answer questions.
arXiv:2606. 17958v1 Announce Type: cross Abstract: Semi-supervised medical image segmentation has emerged as a dominant research problem in medical image analysis, mitigating annotation scarcity by leveraging consistency regularization on unlabeled data.
CasCVS‑Net is a staged multi‑task cascade that jointly performs object detection, semantic segmentation, and Critical View of Safety (CVS) assessment for laparoscopic cholecystectomy. The model couples tasks through predicted anatomy—boxes guide segmentation and masks provide region‑level features for CVS classification—allowing CVS assessment to rely solely on model predictions. Trained on the Endoscapes dataset, CasCVS‑Net outperforms state‑of‑the‑art methods, achieving higher mAP and mIoU scores across detection, segmentation, and CVS tasks, especially for rare hepatocystic structures.
arXiv:2608. 08727v1 Announce Type: cross Abstract: To address this gap, we introduce TomaMMU, a large-scale Tomato leaf disease MultiModal Understanding dataset, alongside TomaBench, a benchmark for evaluating VLMs on tomato disease understanding.
arXiv:2606. 06224v1 Announce Type: cross Abstract: Explanations of multiple instance learning (MIL) models are widely used for validation and discovery in digital histopathology.