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Pneumoperitoneum (free air in the peritoneal cavity) is a rare condition that can be life threatening and require emergency surgery. It can be detected in chest X-ray but there are some challenges associated to this detection, such as small amounts of air that may be missed by a radiologist, or pseudo-pneumoperitoneum (air in the abdomen that may look like pneumoperitoneum). In this work, we propose using an ensemble of deep learning models trained on different subsets of data to boost the classification and generalization performance of the model as well as hard-negative mining to mitigate the effect of pseudo-pneumoperitoneum. We demonstrate superior performance when the model ensemble is utilized as well as good localization of the finding with multiple model explainability techniques.

Characterization of lesions by artificial intelligence (AI) has been the subject of extensive research. In recent years, many studies demonstrated the ability of convolution neural networks (CNNs) to successfully distinguish between malignant and benign breast lesions in mammography (MG) images. However, to date, no study has assessed the specific sub-type of lesions in MG images, as detailed in histolopathology reports. We present a method for finer classification of breast lesions in MG images into multiple pathology sub-types. Our approach works well with radiologists’ diagnostic workflow, and uses data available in radiology reports. The proposed Dual-Radiology Dual-Resolution Network (Du-Rad Du-Res Net) receives dual input from the radiologist and dual image resolutions. The radiologist input includes annotation of the lesion area and semantic radiology features; the dual image resolutions comprise a low resolution of the entire mammogram and a high resolution of the lesion area. The network estimates the likelihood of malignancy, as well as the associated pathological sub-type. We show that the combined input of the lesion region of interest (ROI) and the entire mammogram is important for optimizing the model’s performance. We tested the AI in a reader study on a dataset of 100 heldout cases. The AI outperformed three breast radiologists in the task of lesion histopathology sub-typing.

Purpose To describe magnetic resonance (MR) imaging and MR cholangiopancreatography (MRCP) findings in gallbladder carcinoma, and to correlate these findings with available surgical and biopsy information. Methods Preoperative MR images (T1-weighted spin-echo, T2-weighted fast spin-echo, single shot fast spin-echo, and dynamic gadolinium-enhanced gradient echo) in 34 patients with gallbladder carcinoma were retrospectively reviewed for appearance of the primary neoplasm and for demonstration of hepatic, peritoneal, duodenal, and nodal involvement. Imaging findings were then compared with surgical findings (n = 19 patients) and histologic findings (n = 15 patients). Results Gallbladder carcinoma manifested at MR imaging as focal gallbladder wall thickening with an eccentric mass in 76% (26/34) of cases. The most common types of regional spread demonstrated were direct liver invasion in 91% (31/34), lymphadenopathy in 76% (26/34), and biliary tract invasion in 62% (21/34). Sensitivity for direct hepatic invasion was 100%, and was 92% for lymph node metastasis. Conclusion MRI and MRCP can provide information relevant to preoperative staging of gallbladder carcinoma.