Panagiotis (Panos) Kaliosis

Diagnostic Captioning (DC) automatically generates a diagnostic text from one or more medical images (e.g., X-rays, MRIs) of a patient. Treated as a draft, the generated text may assist clinicians, by providing an initial estimation of the patient’s condition, speeding up and helping safeguard the diagnostic process. The accuracy of a diagnostic text, however, strongly depends on how well the key medical conditions depicted in the images are expressed. We propose a new data-driven guided decoding method that incorporates medical information, in the form of existing tags capturing key conditions of the image(s), into the beam search of the diagnostic text generation process. We evaluate the proposed method on two medical datasets using four DC systems that range from generic image-to-text systems with CNN encoders and RNN decoders to pre-trained Large Language Models. The latter can also be used in few- and zero-shot learning scenarios. In most cases, the proposed mechanism improves performance with respect to all evaluation measures. We provide an open-source implementation of the proposed method at https://github.com/nlpaueb/dmmcs.

We present MagCIL’s approach for the 1st track of the "2nd e-Prevention challenge: Psychotic and Non-Psychotic Relapse Detection using Wearable-Based Digital Phenotyping". First we present our approach for preprocessing and extracting features from the wearable’s raw data. We then propose a Transformer model for learning self-supervised representations from augmented features, trained on data from non-relapse days from each of the 9 patients of the challenge. We adopt two unsupervised methods for detecting relapse days as outliers. A separate unsupervised model is tuned for each patient using the validation data of the challenge. Our method ranked second with ROCAUC = 0.651 and PRAUC = 0.642 on the final test dataset of the challenge. The respective code is available at this repository.