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Digital pathology has recently been revolutionized by advancements in artificial intelligence, deep learning, and high-performance computing. With its advanced tools, digital pathology can help improve and speed up the diagnostic process,…

In the current technological era, the medical profession has emerged as one of the researchers' favorite subject areas, and cancer is one of them. Because there is now no effective treatment for this illness, it is a matter of concern. Only…

The current study of cell architecture of inflammation in histopathology images commonly performed for diagnosis and research purposes excludes a lot of information available on the biopsy slide. In autoimmune diseases, major outstanding…

In this study, we propose a robust methodology for identification of myeloid blasts followed by prediction of genetic mutation in single-cell images of blasts, tackling challenges associated with label accuracy and data noise. We trained an…

Mammography stands as the main screening method for detecting breast cancer early, enhancing treatment success rates. The segmentation of landmark structures in mammography images can aid the medical assessment in the evaluation of cancer…

Gliomas are brain tumor types that have a high mortality rate which means early and accurate diagnosis is important for therapeutic intervention for the tumors. To address this difficulty, the proposed research will develop a hybrid deep…

Breast-Conserving Surgery (BCS) requires precise intraoperative margin assessment to preserve healthy tissue. Deep Ultraviolet Fluorescence Scanning Microscopy (DUV-FSM) offers rapid, high-resolution surface imaging for this purpose;…

In the medical field, accurate diagnosis of lung cancer is crucial for treatment. Traditional manual analysis methods have significant limitations in terms of accuracy and efficiency. To address this issue, this paper proposes a deep…

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…

Deep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically approved applications use this technology. Most approaches, however, predict categorical labels, whereas biomarkers are often continuous measurements.…

Deep learning-based computer-aided diagnosis has achieved unprecedented performance in breast cancer detection. However, most approaches are computationally intensive, which impedes their broader dissemination in real-world applications. In…

Histopathological images (HI) encrypt resolution dependent heterogeneous textures & diverse color distribution variability, manifesting in micro-structural surface tissue convolutions. Also, inherently high coherency of cancerous cells…

Radiation therapy has emerged as one of the preferred techniques to treat brain cancer patients. During treatment, a very high dose of radiation is delivered to a very narrow area. Prescribed radiation therapy for brain cancer requires…

Melanoma is the most aggressive form of skin cancer, and early detection can significantly increase survival rates and prevent cancer spread. However, developing reliable automated detection techniques is difficult due to the lack of…

Automatic lymph node (LN) segmentation and detection for cancer staging are critical. In clinical practice, computed tomography (CT) and positron emission tomography (PET) imaging detect abnormal LNs. Despite its low contrast and variety in…

We evaluate the performance of federated learning (FL) in developing deep learning models for analysis of digitized tissue sections. A classification application was considered as the example use case, on quantifiying the distribution of…

Histopathology remains the gold standard for cancer diagnosis because it provides detailed cellular-level assessment of tissue morphology. However, manual histopathological examination is time-consuming, labour-intensive, and subject to…

Melanoma diagnosed and treated in its early stages can increase the survival rate. A projected increase in skin cancer incidents and a dearth of dermatopathologists have emphasized the need for computational pathology (CPATH) systems. CPATH…

Current Computer-Aided Diagnosis (CAD) methods mainly depend on medical images. The clinical information, which usually needs to be considered in practical clinical diagnosis, has not been fully employed in CAD. In this paper, we propose a…

Early cancer detection remains one of the most critical challenges in modern healthcare, where delayed diagnosis significantly reduces survival outcomes. Recent advancements in artificial intelligence, particularly deep learning, have…