arXiv AI

On-Device Multi-Species Malaria Detection with Uncertainty-Calibrated Slide-Level Aggregation

arXiv:2608. 08566v1 Announce Type: cross Abstract: Malaria remains a leading cause of mortality in resource-limited settings, where expert microscopists are scarce.

arXiv AI

Jul 2

MalariAI: A Label-Resilient Decoupled Framework for Universal Cell Segmentation and Explainable Stage Classification in Dense Malaria Blood Smears

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.

By Kaysarul Anas Apurba, Md Hasibul Hasan, Mohammed Ali, Tanzilur Rahman

computer-vision

arXiv AI

Aug 5

MalariAI: A Label-Resilient Decoupled Framework for Annotation-Agnostic Cell Segmentation and Explainable Stage Classification in Dense Malaria Blood Smears

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.

By Kaysarul Anas Apurba, Md Hasibul Hasan, Mohammed Ali, Tanzilur Rahman

computer-vision

arXiv Machine Learning

Jul 21

SGMCE: Segment-Grounded Morphological Concept Explanation for Malaria Parasite Species Identification in Thick Blood Smears

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.

By Ahmed Tahiru Issah, Charles B. Delahunt, Carine Mukamakuza

llms rag multimodal

arXiv Machine Learning

Jul 7

LeukocyteCount: Automatic Identification and Counting for leukocytes using Deep Learning

arXiv:2607. 04486v1 Announce Type: new Abstract: Diagnosing and monitoring diseases frequently involves the analysis of human biological samples, with blood analysis being pivotal.

By Ahmed M. Sayed (Faculty of Computers and Artificial Intelligence, Helwan University, Cairo, Egypt), Sondos A. Refaat (Faculty of Computers and Artificial Intelligence, Helwan University, Cairo, Egypt), Abdallah M. Mostafa (Faculty of Computers and Artificial Intelligence, Helwan University, Cairo, Egypt), Mariam S. El-Rahmany (Faculty of Computers and Artificial Intelligence, Helwan University, Cairo, Egypt), Ensaf Hussein Mohamed (Faculty of Computers and Artificial Intelligence, Helwan University, Cairo, Egypt, School of Information Technology and Computer Science)

arXiv Machine Learning

Jul 9

Pixel-Precise Explainable Stress Indexing: A Semantic Segmentation Framework for Disease Severity Quantification in Field Crops

arXiv:2607. 06585v1 Announce Type: cross Abstract: Plant diseases, resulting from both biotic and abiotic stresses, cause an estimated 20-40% loss in global agricultural yield annually, resulting in economic damages exceeding USD 220 billion.

By Raunak Kumar, Soumyashree Kar

computer-vision

arXiv AI

Jul 28

A Cascaded Edge-Cloud Architecture for Automated Diabetic Retinopathy Screening

arXiv:2605. 14108v2 Announce Type: replace-cross Abstract: Diabetic Retinopathy (DR) is one of the leading causes of preventable blindness, and automated screening can help extend specialist capacity in resource-constrained clinical workflows.

By Nishi Doshi, Shrey Shah

safety

arXiv AI

Jun 8

Mitosis Detection in the Wild: Multi-Tumor and Context-Aware Generalization in the MIDOG 2025 Challenge

arXiv:2606. 07368v1 Announce Type: cross Abstract: Automated mitosis detection is a well-established task in computational pathology.

By Marc Aubreville, Jonas Ammeling, Sweta Banerjee, Viktoria Weiss, Taryn A. Donovan, Robert Klopfleisch, Jiaqi Lv, Shan E Ahmed Raza, Rapha\"el Bourgade, Thomas Walter, Yasemin Topuz, Song\"ul Varl{\i}, Charles-Antoine Collins-Fekete, Zhuoyan Shen, Navya Sri Kelam, Nitin Singhal, Christian Marzahl, Brian Napora, Tengyou Xu, Hongyan Gu, Mario Vento, Gennaro Percannella, Norbert Ropiak, Izabela Wasiak, Jie Xiao, Shaojun Liu, Seungho Choe, April Khademi, Vidushi Walia, Sujatha Kotte, Andrew Broad, Alex Wright, Guillaume Balezo, Esha Sadia Nasir, Mostafa Jahanifar, Yosuke Yamagishi, Shouhei Hanaoka, Mattia Sarno, Francesco Tortorella, Biwen Meng, Jingxin Liu, Sara Krauss, Daniel Hieber, Lavish Ramchandani, Dev Kumar Das, Mieko Ochi, Yuan Bae, Piotr Giedziun, Mateusz Maniewski, Vangala Govindakrishnan Saipradeep, Naveen Sivadasan, Leire Benito-Del-Valle, Adrian Galdran, Kaustubh Atey, Sameer Anand Jha, Adinath Dukre, Imran Razzak, Maxime W. Lafarge, Viktor H. Koelzer, Nils Porsche, Nikolas Stathonikos, Mitko Veta, Dominik Hirling, Zsanett Zs\'ofia Iv\'an, Peter Horvath, Katharina Breininger, Christof A. Bertram

benchmarks

arXiv AI

Jul 1

Toxicity Assessment in Preclinical Histopathology via Class-Aware Mahalanobis Distance for Known and Novel Anomalies

arXiv:2602. 02124v2 Announce Type: replace-cross Abstract: Drug-induced toxicity is a leading cause of preclinical and early-clinical failure, making early detection critical.

By Olga Graf, Dhrupal Patel, Peter Gro{\ss}, Charlotte Lempp, Matthias Hein, Fabian Heinemann

llms computer-vision fine-tuning safety

arXiv Machine Learning

Aug 7

Benchmark Evaluation of Federated Learning on Multi-organ Images

arXiv:2607. 08219v2 Announce Type: replace-cross Abstract: The privacy requirements of medical data and its substantial variations across organs and modalities hinder the clinical implementation of medical AI.

By Junbin Mao, Xu Tian, Jianchun Zhu, Ludi Li, Jin Liu

benchmarks safety

arXiv AI

Jun 12

Cascade Classification of Dermoscopic Images of Skin Neoplasms with Controllable Sensitivity and External Clinical Validation

arXiv:2606. 13135v1 Announce Type: cross Abstract: Purpose.

By Elena S. Kozachok, Sergey S. Seregin, Aleksandr V. Kozachok, Ilya P. Latyshev, Oleg I. Samovarov

arXiv Machine Learning

Jul 10

Benchmark Evaluation of Feredated Learning on Multi-organ Images

arXiv:2607. 08219v1 Announce Type: cross Abstract: The privacy requirements of medical data and its substantial variations across organs and modalities hinder the clinical implementation of medical AI.

By Junbin Mao, Xu Tian, Jianchun Zhu, Ludi Li, Jin Liu

benchmarks safety

arXiv AI

1d ago

Comprehensive Benchmarking of Deep Learning Architectures for Lung Cancer Histopathology

arXiv:2608. 15915v1 Announce Type: cross Abstract: Lung cancer remains the leading cause of cancer-related mortality worldwide, while histopathological diagnosis is often affected by inter-observer variability and the substantial workload associated with manual slide examination.

By Hadi Hasan, Safaa Salman, Lama Sleem, Ralph Mouawad, Ali Chehab

llms computer-vision benchmarks

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