AIMC Topic: Image Interpretation, Computer-Assisted

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On the evaluation of deep learning interpretability methods for medical images under the scope of faithfulness.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Evaluating the interpretability of Deep Learning models is crucial for building trust and gaining insights into their decision-making processes. In this work, we employ class activation map based attribution methods in a set...

DTDO: Driving Training Development Optimization enabled deep learning approach for brain tumour classification using MRI.

Network (Bristol, England)
A brain tumour is an abnormal mass of tissue. Brain tumours vary in size, from tiny to large. Moreover, they display variations in location, shape, and size, which add complexity to their detection. The accurate delineation of tumour regions poses a ...

Artificial Intelligence Helps Pathologists Increase Diagnostic Accuracy and Efficiency in the Detection of Breast Cancer Lymph Node Metastases.

The American journal of surgical pathology
The detection of lymph node metastases is essential for breast cancer staging, although it is a tedious and time-consuming task where the sensitivity of pathologists is suboptimal. Artificial intelligence (AI) can help pathologists detect lymph node ...

Automated Prediction of Malignant Melanoma using Two-Stage Convolutional Neural Network.

Archives of dermatological research
PURPOSE: A skin lesion refers to an area of the skin that exhibits anomalous growth or distinctive visual characteristics compared to the surrounding skin. Benign skin lesions are noncancerous and generally pose no threat. These irregular skin growth...

Masked hypergraph learning for weakly supervised histopathology whole slide image classification.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Graph neural network (GNN) has been extensively used in histopathology whole slide image (WSI) analysis due to the efficiency and flexibility in modelling relationships among entities. However, most existing GNN-based WSI a...

3DVascNet: An Automated Software for Segmentation and Quantification of Mouse Vascular Networks in 3D.

Arteriosclerosis, thrombosis, and vascular biology
BACKGROUND: Analysis of vascular networks is an essential step to unravel the mechanisms regulating the physiological and pathological organization of blood vessels. So far, most of the analyses are performed using 2-dimensional projections of 3-dime...

Distillation of multi-class cervical lesion cell detection via synthesis-aided pre-training and patch-level feature alignment.

Neural networks : the official journal of the International Neural Network Society
Automated detection of cervical abnormal cells from Thin-prep cytologic test (TCT) images is crucial for efficient cervical abnormal screening using computer-aided diagnosis systems. However, the construction of the detection model is hindered by the...

Deep learning ensembles for detecting brain metastases in longitudinal multi-modal MRI studies.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Metastatic brain cancer is a condition characterized by the migration of cancer cells to the brain from extracranial sites. Notably, metastatic brain tumors surpass primary brain tumors in prevalence by a significant factor, they exhibit an aggressiv...

A review of deep learning-based information fusion techniques for multimodal medical image classification.

Computers in biology and medicine
Multimodal medical imaging plays a pivotal role in clinical diagnosis and research, as it combines information from various imaging modalities to provide a more comprehensive understanding of the underlying pathology. Recently, deep learning-based mu...

SAMCF: Adaptive global style alignment and multi-color spaces fusion for joint optic cup and disc segmentation.

Computers in biology and medicine
The optic cup (OC) and optic disc (OD) are two critical structures in retinal fundus images, and their relative positions and sizes are essential for effectively diagnosing eye diseases. With the success of deep learning in computer vision, deep lear...