Vijay Ram Kumar Papineni

Santosh Sanjeev, Salwa K. Al Khatib, Mai A. Shaaban et al.

Previous deep learning efforts have focused on improving the performance of Pulmonary Embolism(PE) diagnosis from Computed Tomography (CT) scans using Convolutional Neural Networks (CNN). However, the features from CT scans alone are not always sufficient for the diagnosis of PE. CT scans along with electronic heath records (EHR) can provide a better insight into the patients condition and can lead to more accurate PE diagnosis. In this paper, we propose Pulmonary Embolism Detection using Contrastive Learning (PECon), a supervised contrastive pretraining strategy that employs both the patients CT scans as well as the EHR data, aiming to enhance the alignment of feature representations between the two modalities and leverage information to improve the PE diagnosis. In order to achieve this, we make use of the class labels and pull the sample features of the same class together, while pushing away those of the other class. Results show that the proposed work outperforms the existing techniques and achieves state-of-the-art performance on the RadFusion dataset with an F1-score of 0.913, accuracy of 0.90 and an AUROC of 0.943. Furthermore, we also explore the explainability of our approach in comparison to other methods. Our code is publicly available at https://github.com/BioMedIA-MBZUAI/PECon.

Santosh Sanjeev, Fadillah Adamsyah Maani, Arsen Abzhanov et al.

With the emergence of vision language models in the medical imaging domain, numerous studies have focused on two dominant research activities: (1) report generation from Chest X-rays (CXR), and (2) synthetic scan generation from text or reports. Despite some research incorporating multi-view CXRs into the generative process, prior patient scans and reports have been generally disregarded. This can inadvertently lead to the leaving out of important medical information, thus affecting generation quality. To address this, we propose TiBiX: Leveraging Temporal information for Bidirectional X-ray and Report Generation. Considering previous scans, our approach facilitates bidirectional generation, primarily addressing two challenging problems: (1) generating the current image from the previous image and current report and (2) generating the current report based on both the previous and current images. Moreover, we extract and release a curated temporal benchmark dataset derived from the MIMIC-CXR dataset, which focuses on temporal data. Our comprehensive experiments and ablation studies explore the merits of incorporating prior CXRs and achieve state-of-the-art (SOTA) results on the report generation task. Furthermore, we attain on-par performance with SOTA image generation efforts, thus serving as a new baseline in longitudinal bidirectional CXR-to-report generation. The code is available at https://github.com/BioMedIA-MBZUAI/TiBiX.

Mai A. Shaaban, Tausifa Jan Saleem, Vijay Ram Papineni et al.

Medical visual question answering (MedVQA) plays a vital role in clinical decision-making by providing contextually rich answers to image-based queries. Although vision-language models (VLMs) are widely used for this task, they often generate factually incorrect answers. Retrieval-augmented generation addresses this challenge by providing information from external sources, but risks retrieving irrelevant context, which can degrade the reasoning capabilities of VLMs. Re-ranking retrievals, as introduced in existing approaches, enhances retrieval relevance by focusing on query-text alignment. However, these approaches neglect the visual or multimodal context, which is particularly crucial for medical diagnosis. We propose MOTOR, a novel multimodal retrieval and re-ranking approach that leverages grounded captions and optimal transport. It captures the underlying relationships between the query and the retrieved context based on textual and visual information. Consequently, our approach identifies more clinically relevant contexts to augment the VLM input. Empirical analysis and human expert evaluation demonstrate that MOTOR achieves higher accuracy on MedVQA datasets, outperforming state-of-the-art methods by an average of 6.45%. Code is available at https://github.com/BioMedIA-MBZUAI/MOTOR.

Anees Ur Rehman Hashmi, Ibrahim Almakky, Mohammad Areeb Qazi et al.

Large-scale generative models have demonstrated impressive capabilities in producing visually compelling images, with increasing applications in medical imaging. However, they continue to grapple with hallucination challenges and the generation of anatomically inaccurate outputs. These limitations are mainly due to the reliance on textual inputs and lack of spatial control over the generated images, hindering the potential usefulness of such models in real-life settings. In this work, we present XReal, a novel controllable diffusion model for generating realistic chest X-ray images through precise anatomy and pathology location control. Our lightweight method comprises an Anatomy Controller and a Pathology Controller to introduce spatial control over anatomy and pathology in a pre-trained Text-to-Image Diffusion Model, respectively, without fine-tuning the model. XReal outperforms state-of-the-art X-ray diffusion models in quantitative metrics and radiologists' ratings, showing significant gains in anatomy and pathology realism. Our model holds promise for advancing generative models in medical imaging, offering greater precision and adaptability while inviting further exploration in this evolving field. The code and pre-trained model weights are publicly available at https://github.com/BioMedIA-MBZUAI/XReal.

Salma Hassan, Dawlat Akaila, Maryam Arjemandi et al.

In the complex realm of cognitive disorders, Alzheimer's disease (AD) and vascular dementia (VaD) are the two most prevalent dementia types, presenting entangled symptoms yet requiring distinct treatment approaches. The crux of effective treatment in slowing neurodegeneration lies in early, accurate diagnosis, as this significantly assists doctors in determining the appropriate course of action. However, current diagnostic practices often delay VaD diagnosis, impeding timely intervention and adversely affecting patient prognosis. This paper presents an innovative multi-omics approach to accurately differentiate AD from VaD, achieving a diagnostic accuracy of 89.25%. The proposed method segments the longitudinal MRI scans and extracts advanced radiomics features. Subsequently, it synergistically integrates the radiomics features with an ensemble of clinical, cognitive, and genetic data to provide state-of-the-art diagnostic accuracy, setting a new benchmark in classification accuracy on a large public dataset. The paper's primary contribution is proposing a comprehensive methodology utilizing multi-omics data to provide a nuanced understanding of dementia subtypes. Additionally, the paper introduces an interpretable model to enhance clinical decision-making coupled with a novel model architecture for evaluating treatment efficacy. These advancements lay the groundwork for future work not only aimed at improving differential diagnosis but also mitigating and preventing the progression of dementia.

Numan Saeed, Salma Hassan, Shahad Hardan et al.

We present a publicly available multimodal dataset for head and neck cancer research, comprising 1123 annotated Positron Emission Tomography/Computed Tomography (PET/CT) studies from patients with histologically confirmed disease, acquired from 10 international medical centers. All studies contain co-registered PET/CT scans with varying acquisition protocols, reflecting real-world clinical diversity from a long-term, multi-institution retrospective collection. Primary gross tumor volumes (GTVp) and involved lymph nodes (GTVn) were manually segmented by experienced radiation oncologists and radiologists following established guidelines. We provide anonymized NifTi files, expert-annotated segmentation masks, comprehensive clinical metadata, and radiotherapy dose distributions for a patient subset. The metadata include TNM staging, HPV status, demographics, long-term follow-up outcomes, survival times, censoring indicators, and treatment information. To demonstrate its utility, we benchmark three key clinical tasks: automated tumor segmentation, recurrence-free survival prediction, and HPV status classification, using state-of-the-art deep learning models like UNet, SegResNet, and multimodal prognostic frameworks.