AIMC Topic: Female

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Review on Computer Aided Breast Cancer Detection and Diagnosis using Machine Learning Methods on Mammogram Image.

Current medical imaging
Machine Learning (ML) plays an essential part in the research area of medical image processing. The advantages of ML techniques lead to more intelligent, accurate, and automatic computeraided detection (CAD) systems with improved learning capability....

[Clinical study on application of 3D Slicer software assisted domestic frameless stereotactic robot in biopsy of intracranial lesions].

Zhonghua wai ke za zhi [Chinese journal of surgery]
To examine the application value of 3D Slicer software assisted domestic frameless stereotactic robot in biopsy of intracranial lesions. A retrospective analysis was performed on 80 patients who admitted consecutively and underwent intracerebral le...

Segmentation of Clinical Target Volume From CT Images for Cervical Cancer Using Deep Learning.

Technology in cancer research & treatment
Segmentation of clinical target volume (CTV) from CT images is critical for cervical cancer brachytherapy, but this task is time-consuming, laborious, and not reproducible. In this work, we aim to propose an end-to-end model to segment CTV for cervi...

A non-invasive artificial intelligence approach for the prediction of human blastocyst ploidy: a retrospective model development and validation study.

The Lancet. Digital health
BACKGROUND: One challenge in the field of in-vitro fertilisation is the selection of the most viable embryos for transfer. Morphological quality assessment and morphokinetic analysis both have the disadvantage of intra-observer and inter-observer var...

Artificial intelligence in breast cancer histopathology.

Histopathology
This is a review on the use of artificial intelligence for digital breast pathology. A systematic search on PubMed was conducted, identifying 17,324 research papers related to breast cancer pathology. Following a semimanual screening, 664 papers were...

Deep learning radiomics of ultrasonography for differentiating sclerosing adenosis from breast cancer.

Clinical hemorheology and microcirculation
OBJECTIVES: The purpose of our study is to present a method combining radiomics with deep learning and clinical data for improved differential diagnosis of sclerosing adenosis (SA)and breast cancer (BC).

New Horizons: Artificial Intelligence for Digital Breast Tomosynthesis.

Radiographics : a review publication of the Radiological Society of North America, Inc
The use of digital breast tomosynthesis (DBT) in breast cancer screening has become widely accepted, facilitating increased cancer detection and lower recall rates compared with those achieved by using full-field digital mammography (DM). However, th...

Prediction of Breast Cancer Through Random Forest.

Current medical imaging
BACKGROUND: 8% of women are diagnosed with breast cancer. (BC) BC is the second most common cause of death in both developed and undeveloped countries. BC is characterized by the mutation of genes, constant pain, changes in the size, color (redness),...

A deep learning-based method for cervical transformation zone classification in colposcopy images.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Colposcopy is one of the common methods of cervical cancer screening. The type of cervical transformation zone is considered one of the important factors for grading colposcopic findings and choosing treatment.