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Self-supervised learning provides an opportunity to explore unlabeled chest X-rays and their associated free-text reports accumulated in clinical routine without manual supervision. This paper proposes a Joint Image Text Representation Learning Network (JoImTeRNet) for pre-training on chest X-ray images and their radiology reports. The model was pre-trained on both the global image-sentence level and the local image region-word level for visual-textual matching. Both are bidirectionally constrained on Cross-Entropy based and ranking-based Triplet Matching Losses. The region-word matching is calculated using the attention mechanism without direct supervision about their mapping. The pre-trained multi-modal representation learning paves the way for downstream tasks concerning image and/or text encoding. We demonstrate the representation learning quality by cross-modality retrievals and multi-label classifications on two datasets: OpenI-IU and MIMIC-CXR, Comment: 10 Pages, 1 Figure, 3 Tables, Accepted in 12th Machine Learning in Medical Imaging (MLMI 2021) workshop

In clinical applications, neural networks must focus on and highlight the most important parts of an input image. Soft-Attention mechanism enables a neural network toachieve this goal. This paper investigates the effectiveness of Soft-Attention in deep neural architectures. The central aim of Soft-Attention is to boost the value of important features and suppress the noise-inducing features. We compare the performance of VGG, ResNet, InceptionResNetv2 and DenseNet architectures with and without the Soft-Attention mechanism, while classifying skin lesions. The original network when coupled with Soft-Attention outperforms the baseline[16] by 4.7% while achieving a precision of 93.7% on HAM10000 dataset [25]. Additionally, Soft-Attention coupling improves the sensitivity score by 3.8% compared to baseline[31] and achieves 91.6% on ISIC-2017 dataset [2]. The code is publicly available at github., Comment: 8 pages, 9 figures, 4 tables

The task of writer verification is to provide a likelihood score for whether the queried and known handwritten image samples belong to the same writer or not. Such a task calls for the neural network to make it's outcome interpretable, i.e. provide a view into the network's decision making process. We implement and integrate cross-attention and soft-attention mechanisms to capture the highly correlated and salient points in feature space of 2D inputs. The attention maps serve as an explanation premise for the network's output likelihood score. The attention mechanism also allows the network to focus more on relevant areas of the input, thus improving the classification performance. Our proposed approach achieves a precision of 86\% for detecting intra-writer cases in CEDAR cursive "AND" dataset. Furthermore, we generate meaningful explanations for the provided decision by extracting attention maps from multiple levels of the network., Comment: 7 pages, 6 figures, Published in 2020 17th International Conference on Frontiers in Handwriting Recognition (ICFHR)

The pattern of Electroencephalogram (EEG) signal differs significantly across different subjects, and poses challenge for EEG classifiers in terms of 1) effectively adapting a learned classifier onto a new subject, 2) retaining knowledge of known subjects after the adaptation. We propose an efficient transfer learning method, named Meta UPdate Strategy (MUPS-EEG), for continuous EEG classification across different subjects. The model learns effective representations with meta update which accelerates adaptation on new subject and mitigate forgetting of knowledge on previous subjects at the same time. The proposed mechanism originates from meta learning and works to 1) find feature representation that is broadly suitable for different subjects, 2) maximizes sensitivity of loss function for fast adaptation on new subject. The method can be applied to all deep learning oriented models. Extensive experiments on two public datasets demonstrate the effectiveness of the proposed model, outperforming current state of the arts by a large margin in terms of both adapting on new subject and retain knowledge of learned subjects.

Deep learning system have drawback that their output is not accompanied with ex-planation. In a domain such as forensic handwriting verification it is essential to provideexplanation to jurors. The goal of handwriting verification is to find a measure of confi-dence whether the given handwritten samples are written by the same or different writer.We propose a method to generate explanations for the confidence provided by convolu-tional neural network (CNN) which maps the input image to 15 annotations (features)provided by experts. Our system comprises of: (1) Feature learning network (FLN),a differentiable system, (2) Inference module for providing explanations. Furthermore,inference module provides two types of explanations: (a) Based on cosine similaritybetween categorical probabilities of each feature, (b) Based on Log-Likelihood Ratio(LLR) using directed probabilistic graphical model. We perform experiments using acombination of feature learning network (FLN) and each inference module. We evaluateour system using XAI-AND dataset, containing 13700 handwritten samples and 15 cor-responding expert examined features for each sample. The dataset is released for publicuse and the methods can be extended to provide explanations on other verification taskslike face verification and bio-medical comparison. This dataset can serve as the basis and benchmark for future research in explanation based handwriting verification. The code is available on github., Comment: Presented at BMVC 2019: Workshop on Interpretable and Explainable Machine Vision, Cardiff, UK