Computational cytology is a critical, rapid-develo**, yet challenging topic in medical
image computing concerned with analyzing digitized cytology images by computer-aided …

Breast cancer has reached the highest incidence rate worldwide among all malignancies
since 2020. Breast imaging plays a significant role in early diagnosis and intervention to …

Medical anomaly detection is a crucial yet challenging task aimed at recognizing abnormal
images to assist in diagnosis. Due to the high-cost annotations of abnormal images, most …

Abstract Universal Lesion Detection (ULD) in computed tomography plays an essential role
in computer-aided diagnosis. Promising ULD results have been reported by multi-slice-input …

Deep learning has seen rapid growth in recent years and achieved state-of-the-art
performance in a wide range of applications. However, training models typically requires …

The task of multimodal cancer detection is to determine the locations and categories of
lesions by using different imaging techniques, which is one of the key research methods for …

Chest X-ray (CXR) is the most typical radiological exam for diagnosis of various diseases.
Due to the expensive and time-consuming annotations, detecting anomalies in CXRs in an …

Accurate localization of abnormalities within X-ray images is of the utmost importance for
arriving at the correct diagnosis. Weakly supervised learning (WSL) aims to train deep …

Accurate abnormality localization in chest X-rays (CXR) can benefit the clinical diagnosis of
various thoracic diseases. However, the lesion-level annotation can only be performed by …

Most of the existing object detection works are based on the bounding box annotation: each
object has a precise annotated box. However, for rib fractures, the bounding box annotation …