No abstract

Every year physicians face an increasing demand of image-based diagnosis from patients, a problem that can be addressed with recent artificial intelligence methods. In this context, we survey works in the area of automatic report generation from medical images, with emphasis on methods using deep neural networks, with respect to: (1) Datasets, (2) Architecture Design, (3) Explainability and (4) Evaluation Metrics. Our survey identifies interesting developments, but also remaining challenges. Among them, the current evaluation of generated reports is especially weak, since it mostly relies on traditional Natural Language Processing (NLP) metrics, which do not accurately capture medical correctness.

Urachal carcinoma is an uncommon tumor, with a poor prognosis. The most common histological type is adenocarcinoma, which may produce mucus. The symptoms aren't specific, such as hematuria or abdominal mass. Diagnosis can be made endoscopically with a biopsy or by echography and above all by computerized tomography. The treatment of choice is cystectomy with lymphadenectomy or segmental resection of the bladder to. The Authors refer to a patient with urachal carcinoma, stage IIIA involving the bladder.

Several deep learning architectures have been proposed over the last years to deal with the task of generating a written report given an imaging exam as input. Most works evaluate the generated reports using standard Natural Language Processing (NLP) metrics (e.g. BLEU, ROUGE), reporting significant progress. This article contrast this progress by comparing state of the art (SOTA) models against weak baselines. We show that simple and even naive approaches yield near SOTA performance on most traditional NLP metrics. We conclude that evaluation methods in this task should be further studied towards correctly measuring clinical accuracy, involving physicians to contribute to this end.

A rare case of hydatid cyst of the left psoas muscle with a short account of the nosological, physiopathological and therapeutical aspects due to muscular echinococcosis is reported. The Authors particularly emphasize the diagnostic difficulties due to discrepancies between radiological imaging and serological data and specially recommend prudence in surgical management.