相关论文: Classification of Luminal Subtypes in Full Mammogr…

Breast cancer is one of the deadliest cancer worldwide. Timely detection could reduce mortality rates. In the clinical routine, classifying benign and malignant tumors from ultrasound (US) imaging is a crucial but challenging task. An…

Fine-grained glomerular subtyping is central to kidney biopsy interpretation, but clinically valuable labels are scarce and difficult to obtain. Existing computational pathology approaches instead tend to evaluate coarse diseased…

Deep neural networks have introduced significant advancements in the field of machine learning-based analysis of digital pathology images including prostate tissue images. With the help of transfer learning, classification and segmentation…

Deep neural networks exhibit limited generalizability across images with different entangled domain features and categorical features. Learning generalizable features that can form universal categorical decision boundaries across domains is…

Purpose: To develop and evaluate the accuracy of a multi-view deep learning approach to the analysis of high-resolution synthetic mammograms from digital breast tomosynthesis screening cases, and to assess the effect on accuracy of image…

In this work we introduce a novel medical image style transfer method, StyleMapper, that can transfer medical scans to an unseen style with access to limited training data. This is made possible by training our model on unlimited…

Medical imaging is an essential tool in many areas of medical applications, used for both diagnosis and treatment. However, reading medical images and making diagnosis or treatment recommendations require specially trained medical…

This research introduces a sophisticated transfer learning model based on Google's MobileNetV2 for breast cancer tumor classification into normal, benign, and malignant categories, utilizing a dataset of 1576 ultrasound images (265 normal,…

Standard classification methods based on handcrafted morphological and texture features have achieved good performance in breast mass differentiation in ultrasound (US). In comparison to deep neural networks, commonly perceived as…

Purpose: A major barrier to the implementation of artificial intelligence for medical applications is the lack of explainability and high confidence for incorrect decisions, specifically with out-of-domain samples. We propose a…

There is a strong need for automated systems to improve diagnostic quality and reduce the analysis time in histopathology image processing. Automated detection and classification of pathological tissue characteristics with computer-aided…

Several studies indicate that deep learning models can learn to detect breast cancer from mammograms (X-ray images of the breasts). However, challenges with overfitting and poor generalisability prevent their routine use in the clinic.…

Deep learning has emerged as a prominent field in recent literature, showcasing the introduction of models that utilize transfer learning to achieve remarkable accuracies in the classification of brain tumor MRI images. However, the…

Purpose: The gold standard for colorectal cancer metastases detection in the peritoneum is histological evaluation of a removed tissue sample. For feedback during interventions, real-time in-vivo imaging with confocal laser microscopy has…

Breast cancer is the most common cancer in women worldwide. The most common screening technology is mammography. To reduce the cost and workload of radiologists, we propose a computer aided detection approach for classifying and localizing…

Cosmic-ray muon sources exhibit distinct scattering angle distributions when interacting with materials of different atomic numbers (Z values), facilitating the identification of various Z-class materials, particularly those radioactive…

Several machine learning techniques for accurate detection of skin cancer from medical images have been reported. Many of these techniques are based on pre-trained convolutional neural networks (CNNs), which enable training the models based…

In order to support the creation of reliable machine learning models for anomaly detection, this project focuses on preprocessing, enhancing, and organizing a medical imaging dataset. There are two classifications in the dataset: normal and…

Reviewing radiology reports in emergency departments is an essential but laborious task. Timely follow-up of patients with abnormal cases in their radiology reports may dramatically affect the patient's outcome, especially if they have been…

Breast cancer is one of the most threatening diseases in women's life; thus, the early and accurate diagnosis plays a key role in reducing the risk of death in a patient's life. Mammography stands as the reference technique for breast…