ACapMed: Automatic Captioning for Medical Imaging

Abstract

Medical image captioning is a very challenging task that has been rarely addressed in the literature on natural image captioning. Some existing image captioning techniques exploit objects present in the image next to the visual features while generating descriptions. However, this is not possible for medical image captioning when one requires following clinician-like explanations in image content descriptions. Inspired by the preceding, this paper proposes using medical concepts associated with images, in accordance with their visual features, to generate new captions. Our end-to-end trainable network is composed of a semantic feature encoder based on a multi-label classifier to identify medical concepts related to images, a visual feature encoder, and an LSTM model for text generation. Beam search is employed to ensure the best selection of the next word for a given sequence of words based on the merged features of the medical image. We evaluated our proposal on the ImageCLEF medical captioning dataset, and the results demonstrate the effectiveness and efficiency of the developed approach.

Explainability in medical image captioning

“…BLEU score ImageSem [29] 25.7% "Show, Attend and Tell" [16] 43.7% Attention-based encoder-decoder [9] 28.7% Our proposal 40.29%…”

“…Motivated by this, we extend our proposed model [9] for the ImageCLEFmedical 2021 [10], [11] by adding an explainability module. We present therefore, an attention-based encoderdecoder model for medical image captioning.…”

Sequential Brain CT Image Captioning Based on the Pre-Trained Classifiers and a Language Model

“…Recent studies worldwide have reported additional challenges, including (1) a fourfold increase in the workload of radiologists from 2006 to 2020 [8]; (2) the potential decrease in the accuracy of CT image analysis due to the increased workload [9]; (3) a minimum of 30 min required by radiologists to write an interpretation report after analyzing CT images [10]. Therefore, there is a growing effort in research for the image captioningbased automation of medical image analysis to assist physicians by reducing the time required for radiologists to write interpretation reports and streamlining the diagnostic and treatment processes [11][12][13][14][15][16][17].…”

“…Studies have used MIMIC-CXR [18], Open-I [19], MS-COCO [20], and ImageCLEF [21] datasets, which are publicly available training datasets for various types of medical images, as well as chest X-ray images. Reference [11] designs three encoders to extract the following feature vectors: (1) visual feature vector (using VGG-16 [22]): vector representation for medical images, (2) semantic feature vector (using VGG-16): vector representation for the classification results and information about the imaging method of medical images, (3) caption vector (using NLTK [23]): vector representation for captions about medical images. These encoder vectors are concatenated and fed into a decoder, which is LSTM (long short-term memory [24]), to generate texts through a beam search method.…”