AIMC Topic: Biopsy

Clear Filters Showing 121 to 130 of 441 articles

Screening of normal endoscopic large bowel biopsies with interpretable graph learning: a retrospective study.

Gut
OBJECTIVE: To develop an interpretable artificial intelligence algorithm to rule out normal large bowel endoscopic biopsies, saving pathologist resources and helping with early diagnosis.

Deep learning-based multi-model approach on electron microscopy image of renal biopsy classification.

BMC nephrology
BACKGROUND: Electron microscopy is important in the diagnosis of renal disease. For immune-mediated renal disease diagnosis, whether the electron-dense granule is present in the electron microscope image is of vital importance. Deep learning methods ...

Detection of malignancy in whole slide images of endometrial cancer biopsies using artificial intelligence.

PloS one
In this study we use artificial intelligence (AI) to categorise endometrial biopsy whole slide images (WSI) from digital pathology as either "malignant", "other or benign" or "insufficient". An endometrial biopsy is a key step in diagnosis of endomet...

Enhancing Nodule Biopsy Through Technology Integration.

Innovations (Philadelphia, Pa.)
Technology in navigating to peripheral pulmonary nodules has improved in recent years. The recent integration of a robotic platform using shape-sensing technology and mobile cone-beam computed tomography imaging technology has enhanced confidence in ...

EBHI: A new Enteroscope Biopsy Histopathological H&E Image Dataset for image classification evaluation.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
BACKGROUND AND PURPOSE: Colorectal cancer has become the third most common cancer worldwide, accounting for approximately 10% of cancer patients. Early detection of the disease is important for the treatment of colorectal cancer patients. Histopathol...

Artificial Intelligence in Bone Marrow Histological Diagnostics: Potential Applications and Challenges.

Pathobiology : journal of immunopathology, molecular and cellular biology
The expanding digitalization of routine diagnostic histological slides holds a potential to apply artificial intelligence (AI) to pathology, including bone marrow (BM) histology. In this perspective, we describe potential tasks in diagnostics that ca...

Spatiotemporal analysis of speckle dynamics to track invisible needle in ultrasound sequences using convolutional neural networks: a phantom study.

International journal of computer assisted radiology and surgery
PURPOSE: Accurate needle placement into the target point is critical for ultrasound interventions like biopsies and epidural injections. However, aligning the needle to the thin plane of the transducer is a challenging issue as it leads to the decay ...

Rapid, label-free histopathological diagnosis of liver cancer based on Raman spectroscopy and deep learning.

Nature communications
Biopsy is the recommended standard for pathological diagnosis of liver carcinoma. However, this method usually requires sectioning and staining, and well-trained pathologists to interpret tissue images. Here, we utilize Raman spectroscopy to study hu...

Deep learning as a new tool in the diagnosis of mycosis fungoides.

Archives of dermatological research
Mycosis Fungoides (MF) makes up the most of the cutaneous lymphomas. As a malignant disease, the greatest diagnostical challenge is to timely differentiate MF from inflammatory diseases. Contemporary computational methods successfully identify cell n...