Optimal Deep Transfer Learning-Based Human-Centric Biomedical Diagnosis for Acute Lymphoblastic Leukemia Detection

Article Computer Science, Artificial Intelligence

Secure medical digital twin via human-centric interaction and cyber vulnerability resilience

Jun Zhang et al.

Summary: Medical digital twin (MDT) is a virtual replica of a person that utilizes new technologies to predict health status and provide clinical advice. Ensuring the safety of MDT involves understanding digital twin design and implementing a vulnerability tolerant approach. By combining Haptic-AR navigation and deep learning, a new MDT achieves virtual replica and cyber-human interaction, demonstrating potential in cyber resilience.

CONNECTION SCIENCE (2022)

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Article Pathology

Unsupervised computational detection of measurable residual disease in chronic lymphocytic leukaemia

Phillip C. Nguyen et al.

PATHOLOGY (2022)

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Article Pathology

Machine learning and augmented human intelligence use in histomorphology for haematolymphoid disorders

Ahmad Nanaa et al.

Summary: Advances in digital pathology have provided opportunities for decision support using artificial intelligence (AI), with AI applications showing promise in the diagnosis of haematological disorders. AI-based applications in haematopathology have already covered the diagnosis of leukemia, lymphoma, and ancillary testing modalities like flow cytometry.

PATHOLOGY (2021)

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Article Computer Science, Interdisciplinary Applications

A deep learning model (ALNet) for the diagnosis of acute leukaemia lineage using peripheral blood cell images

Laura Boldu et al.

Summary: This study aimed to develop a deep learning system using blood cell images to predict the diagnosis of acute leukemia. The system, ALNet, showed high diagnostic prediction accuracy for myeloid and lymphoid leukemia through the recognition of abnormal cells and differentiation of lineage. It is proposed as a tool to assist clinical pathologists in leukemia diagnosis.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2021)

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Article Computer Science, Artificial Intelligence

Attention-based BiLSTM fused CNN with gating mechanism model for Chinese long text classi fi cation

Jianfeng Deng et al.

Summary: This paper proposes a new text classification model called ABLG-CNN, which utilizes attention mechanism, BiLSTM, CNN, and gating mechanism to effectively capture text context and local phrase features, demonstrating excellent performance in long text classification.

COMPUTER SPEECH AND LANGUAGE (2021)

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Article Computer Science, Information Systems