MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network (1707.02485v1)

Abstract: The inability to interpret the model prediction in semantically and visually meaningful ways is a well-known shortcoming of most existing computer-aided diagnosis methods. In this paper, we propose MDNet to establish a direct multimodal mapping between medical images and diagnostic reports that can read images, generate diagnostic reports, retrieve images by symptom descriptions, and visualize attention, to provide justifications of the network diagnosis process. MDNet includes an image model and a LLM. The image model is proposed to enhance multi-scale feature ensembles and utilization efficiency. The LLM, integrated with our improved attention mechanism, aims to read and explore discriminative image feature descriptions from reports to learn a direct mapping from sentence words to image pixels. The overall network is trained end-to-end by using our developed optimization strategy. Based on a pathology bladder cancer images and its diagnostic reports (BCIDR) dataset, we conduct sufficient experiments to demonstrate that MDNet outperforms comparative baselines. The proposed image model obtains state-of-the-art performance on two CIFAR datasets as well.

• The paper presents MDNet, a multimodal deep learning network that maps medical images to detailed diagnostic reports with enhanced visual attention for improved interpretability.
• The image model leverages multi-scale ensemble connections and refined CNN architectures to effectively capture complex features and boost classification efficiency.
• The language component integrates LSTM-based attention mechanisms to align textual diagnostics with specific image regions, aiding clinical decision-making.

MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network

MDNet addresses a critical shortcoming in the field of computer-aided diagnosis: the lack of semantic and visual interpretability in model predictions. This paper introduces MDNet, a multimodal deep learning model that effectively establishes a direct mapping between medical images and diagnostic reports. The network is robust, with the capability to read images, generate detailed diagnostic reports, and provide visual attention maps to justify its diagnostic process. These features offer substantial improvements over traditional model classification paradigms, which typically obscure their decision-making rationale.

Technical Overview and Results

MDNet is composed of an integrated image-LLM specifically designed for medical image diagnosis. The image model enhances feature capture through multi-scale ensembles and optimizes feature utilization. A novel language component, incorporating a refined attention mechanism, enables the extraction of discriminative image features directly from textual reports, facilitating a mapping from written reports to specific image pixels. The authors apply this network to a dataset of pathology bladder cancer images with their associated diagnostic reports (BCIDR dataset). Their empirical analysis demonstrates that MDNet achieves superior performance metrics against baseline methods, even extending state-of-the-art results achieved by the image model on standard CIFAR datasets.

Image Model and its Contributions

The image model in MDNet leverages the foundational principles of convolutional neural networks (CNNs) to deal with the diversity in feature scales within medical imagery. By analyzing and addressing the constraints within residual networks (ResNets), the authors devised 'ensemble-connections'—an architectural redesign to facilitate superior multi-scale representation integration. This modification allows for independent classification of ensemble outputs, boosting the network's feature utilization efficiency, a claim substantiated by the model's performance on CIFAR-10 and CIFAR-100 datasets.

Language and Attention Mechanisms

The LLM component of MDNet incorporates Long Short-Term Memory (LSTM) networks, a tool of choice for sequence modeling in neural networks. The model's optimization includes training procedures to guide the CNN's learning process through gradients computed from LSTM outputs. A pivotal aspect here is the integration of an auxiliary attention sharpening module, enhancing traditional attention mechanisms to focus predominantly on the most informative image regions, which substantially aids interpretability.

Insights and Implications

The paper prominently highlights the advancement in interpretability within deep learning frameworks for medical diagnosis. By offering a mechanism to not only interpret but also semantically align diagnostic reasoning with visual evidence through generated reports and attention maps, MDNet positions itself as a significant stride forward. It serves not only the practical field of aiding diagnosticians in medical practices but also opens further avenues for research in improving network transparency and verifiability, critical aspects for AI applications in healthcare.

Future Directions

The potential expansions for MDNet include scaling to larger and more diverse datasets, addressing pathologies beyond bladder cancer. Further developments may explore improved biomarker localization and the model’s application to whole-slide images, which present unique challenges due to their scale and variability. As such, MDNet stands as both a novel diagnostic aid and a foundation for further research into interpretable AI in medicinal contexts.