Digital Imaging for Blood Diseases

Últimos artículos

• LukeNet: A lightweight CNN integrated with an XAI model for Smart acute lymphoblastic leukemia detection and management

  Md Taimur Ahad (Department of Management Information Systems, North South University, Bangladesh) · 29 de septiembre de 2026

  Acute Lymphoblastic Leukemia (ALL) patients require early, accurate detection to enable timely treatment and effective patient management. A Convolutional Neural Network (CNN) is well-suited for creating an end-to-end enabling environment for ALL detection and classification. However, most CNN-based…

• Investigating White Blood Cells as a Source of False-Positive Malaria Parasite Detection in African Blood-Smear Images

  Samuel A. Adeniji, Goodness C. Obasi, Chris-Victor Ntwali, Aondana M. Iorumbur, Confidence Raymond, Lowami Uwimana, Ahmed Tahiru Issah · 25 de septiembre de 2026

  White blood cells (WBCs) present on every Giemsa-stained thick blood smear share visual properties with early-stage Plasmodium falciparum ring-form trophozoites: small size, round morphology, and intense purple staining. They are a plausible but untested source of false positives in parasite-only de…

• CoSWA-YOLOv12: Scale-Invariant Tiny Object Detection and Segmentation of Malaria Parasites

  Ahmed Tahiru Issah, Carine Mukamakuza · 25 de septiembre de 2026

  Automated microscopy could widen access to malaria diagnosis in low-resource settings, but the deadliest species, P. falciparum, presents in its early ring stage as an object only a few tens of pixels wide. Such tiny targets are systematically under-detected: overlap-based label assignment starves t…

• Beyond Accuracy: Uncertainty-Guided Boundary Refinement for Reliable Biomedical Image Segmentation

  Anima Kujur · 14 de septiembre de 2026

  Accurate biomedical image segmentation requires not only high global overlap but also reliable delineation of clinically meaningful boundaries. In blood-smear microscopy, cytoplasm and nucleus contours provide the structural basis for downstream morphology analysis; however, deep segmentation models…

• HemaHier: Chain-Conditioned Ordinal Hierarchies for Lineage-Aware Bone-Marrow Cytology

  Afshin Bozorgpour, Peter Sch\"uffler, Edgar Jost, Dorit Merhof · 14 de septiembre de 2026

  Bone-marrow cytology is inherently structured: each cell belongs to a hematopoietic lineage, and many cell types lie on ordered maturation trajectories. Standard flat classifiers ignore this structure, treating a mild same-lineage confusion the same as a severe cross-lineage mistake and predicting o…

• Can You Trust Frozen Hematology Foundation Models under Acquisition Shift?

  Jai Kumar Sharma, Peeyush Tapadiya · 27 de agosto de 2026

  Frozen hematology foundation-model (FM) embeddings reach near-saturated in-domain white-blood-cell (WBC) accuracy, but clinical deployment demands reliability across scanners, sites, stains and preparation pipelines. We audit 15 frozen encoders (hematology, pathology, and general vision) across four…

• EMFE: A lightweight, explainable machine learning framework for malaria cell classification

  Md Abdullah Al Kafi, Walayat Hussain, Mousumi Karmakar, Sumit Kumar Banshal, Ahmed Al Marouf · 26 de agosto de 2026

  Automated malaria diagnosis from stained blood-smear microscopy is dominated by deep convolutional neural networks that are accurate but computationally expensive, poorly interpretable, and rarely validated with patient-level rigor. We present EMFE (Efficient Mathematical Feature Extraction), a five…

• A Hybrid Framework of Vision Transformer and Gated Recurrent Unit for Detection of Mosquito Diseases

  Danial Sharifrazi, Saadat Behzadi, Nouman Javed, Roohallah Alizadehsani, Prasad N. Paradkar, Asim Bhatti · 13 de agosto de 2026

  Identifying dengue virus-infected mosquitoes from control mosquitoes is a major challenge in analyzing mosquito locomotion behavior due to the small size and complexity of the video background. Conventional AI methods are often unable to extract accurate features from video frames and produce errone…

• Retrieval-Augmented Vision Foundation Models for Robust Leukemia Cell Classification across Multiple Microscopy Datasets

  Carlos Zamora, Hiram Zuniga, Ulises Orozco-Rosas, Kenia Picos · 12 de agosto de 2026

  Leukemia cell image classification is challenged by real-world domain shifts from acquisition, staining, illumination, and site protocols, causing single-dataset models to generalize poorly in real clinical scenarios. This work presents a robust framework for leukemia classification across multiple …

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

  Idaya Seidu, Ahmed Tahiru Issah, Charles B. Delahunt, Carine Mukamakuza · 11 de agosto de 2026

  Malaria remains a leading cause of mortality in resource-limited settings, where expert microscopists are scarce. Automated diagnosis based on microscopy images thus has strong potential to improve care delivery. But for an algorithm to deploy, a necessary requirement is that it meet a suite of non-…

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

  Ahmed Tahiru Issah, Charles B. Delahunt, Carine Mukamakuza · 21 de julio de 2026

  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…

• LeukocyteCount: Automatic Identification and Counting for leukocytes using Deep Learning

  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) · 7 de julio de 2026

  Diagnosing and monitoring diseases frequently involves the analysis of human biological samples, with blood analysis being pivotal. Specifically, leukocytes, or white blood cells (WBCs), are essential markers for evaluating the body's defense mechanisms against infections. Traditional methods for WB…

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

  Kaysarul Anas Apurba, Md Hasibul Hasan, Mohammed Ali, Tanzilur Rahman · 2 de julio de 2026

  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. Three compounding failure modes prevent reliable clinical deploymen…

• A Leakage-Aware Comparative Benchmark of Machine Learning, Deep Learning, and Transformer Models for Reliable Leukemia Detection

  Nisreen Albzour · 24 de junio de 2026

  Automated classification of acute lymphoblastic leukemia (ALL) from peripheral blood smear images has often reported near-perfect performance on the C-NMC 2019 dataset. We show that such results can be inflated by patient-level data leakage caused by random image-level partitioning, where cells from…

• Equivariant Representation Learning via Class-Pose Decomposition

  Giovanni Luca Marchetti, Gustaf Tegn\'er, Anastasiia Varava, Danica Kragic · 15 de junio de 2026

  We introduce a general method for learning representations that are equivariant to symmetries of data. Our central idea is to decompose the latent space into an invariant factor and the symmetry group itself. The components semantically correspond to intrinsic data classes and poses respectively. Th…

• CellNet -- Localizing Cells using Sparse and Noisy Point Annotations

  Benjamin Eckhardt, Dmytro Fishman, Stuart Fawke, Andrew Curtis, Bo Fussing, Constantin Pape · 11 de junio de 2026

  Counting living cells is an important step in many biological research workflows. Our collaborators at the Wellcome Sanger Institute study vital genes in humans via large scale saturation genome editing screening, which requires repeatedly counting cells a great number of times. Computer Vision base…

• Patient-Level Diagnosis of Acute Myeloid Leukemia via Deep Learning Analysis of Bone Marrow Smear

  Yuqi Ma, Tianyi Wang, Weihua Meng, Hongru Chen, Fajin Tao, Qunxian Lu, Lin An, Xiaodong Mo, Gen Yang · 10 de junio de 2026

  Bone marrow smear review remains important for acute myeloid leukemia (AML) assessment, but manual single-cell interpretation is labor-intensive and patient-level diagnosis requires aggregation of many cellular observations. We present a cell-to-patient deep learning pipeline for AML-assisted diagno…

• Beyond Accuracy: Evaluating Efficiency, Robustness and Explainability in Deep Learning for Malaria Diagnosis

  Olivier Kanamugire, Kerol Djoumessi · 1 de junio de 2026

  Malaria remains a leading cause of mortality in sub-Saharan Africa, where scarce diagnostic infrastructure makes timely, accurate diagnosis particularly challenging. While deep learning offers a compelling path toward automated malaria screening, clinical adoption is hindered by computational cost a…

• Genetically Aligned Patient Representations Improve Hematological Diagnosis

  Muhammed Furkan Dasdelen, Fatih Ozlugedik, Ilaria Looser, Rao Muhammad Umer, Christian Pohlkamp, Carsten Marr · 29 de mayo de 2026

  Multimodal alignment of histopathology encoders with transcriptomic and genomic data has been shown to significantly improve performance in downstream diagnostic tasks. Hematological cytology is unique in that visual single-cell evaluation is often paired with cytogenetics and molecular genetics for…

• WBCAtt+: Fine-Grained Pixel-Level Morphological Annotations for White Blood Cell Images

  Satoshi Tsutsui, Winnie Pang, Shuting He, Bihan Wen · 20 de mayo de 2026

  The microscopic examination of white blood cells (WBCs) plays a fundamental role in pathology and is essential for diagnosing blood disorders such as leukemia and anemia. To support further research on WBC images, multiple datasets have been proposed. However, they mainly annotate cell categories, a…

• A Logistic Regression Model to Predict Malaria Severity in Children

  Mary Opokua Ansong, Asare Yaw Obeng, Samuel King Opoku · 20 de mayo de 2026

  One of the main causes of death around the globe is malaria. Researchers have sought to develop predictive models for malaria outbreaks based on meteorological data, climate data and the breeding cycle of Plasmodium, the causative agent of malaria. This study predicts the severity of malaria based o…

• PRISM: Perinuclear Ring-based Image Segmentation Method for Acute Lymphoblastic Leukemia Classification

  Larissa Ferreira Rodrigues Moreira, Leonardo Gabriel Ferreira Rodrigues, Rodrigo Moreira, Andr\'e Ricardo Backes · 14 de mayo de 2026

  Automated analysis of peripheral blood smears for Acute Lymphoblastic Leukemia (ALL) is hindered by low contrast and substantial variability in cytoplasmic appearance, which complicate conventional membrane-based segmentation. We found that many recent approaches rely on heavy neural architectures a…

• A Hierarchical Ensemble Inference Pipeline for Robust White Blood Cell Classification Under Domain Shifts

  Ruyi Dai, Tingkwong Ng, Hao Chen · 28 de abril de 2026

  Automated white blood cell (WBC) classification is essential for scalable leukaemia screening. However, real-world deployment is challenged by domain shifts caused by staining protocols, scanner characteristics, and inter-laboratory variability, which often degrade model performance. The White Blood…

• Multi-Beholder: Biomarker Prediction for Low-Grade Glioma with Multiple Instance Learning and One-Class Classification

  Zijie Fang, Yihan Liu, Yifeng Wang, Xiangyang Zhang, Yang Chen, Changjing Cai, Yiyang Lin, Ying Han, Zhi Wang, Shan Zeng, Jun Tan, Yongbing Zhang, Hong Shen · 21 de abril de 2026

  Biomarker detection is an indispensable part of the diagnosis and treatment of low-grade glioma (LGG). However, current LGG biomarker detection methods rely on expensive and complex molecular genetic testing, for which professionals are required to analyze the results, and intra-rater variability is…

• Early Detection of Acute Myeloid Leukemia (AML) Using YOLOv12 Deep Learning Model

  Enas E. Ahmed, Salah A. Aly, Mayar Moner · 20 de abril de 2026

  Acute Myeloid Leukemia (AML) is one of the most life-threatening type of blood cancers, and its accurate classification is considered and remains a challenging task due to the visual similarity between various cell types. This study addresses the classification of the multiclasses of AML cells Utili…