Accurate cancer survival prediction requires integration of diverse data modalities that reflect the complex interplay between imaging, clinical parameters, and textual reports. However, existing multimodal approaches suffer from simplistic fusion strategies, massive computational requirements, and lack of interpretability-critical barriers to clinical adoption. We present EAGLE (Efficient Alignment of Generalized Latent Embeddings), a novel deep learning framework that addresses these limitations through attention-based multimodal fusion with comprehensive attribution analysis. EAGLE introduces four key innovations: (1) dynamic cross-modal attention mechanisms that learn hierarchical relationships between modalities, (2) massive dimensionality reduction (99.96%) while maintaining predictive performance, (3) three complementary attribution methods providing patient-level interpretability, and (4) a unified pipeline enabling seamless adaptation across cancer types. We evaluated EAGLE on 911 patients across three distinct malignancies: glioblastoma (GBM, n=160), intraductal papillary mucinous neoplasms (IPMN, n=171), and non-small cell lung cancer (NSCLC, n=580). Patient-level analysis showed high-risk individuals relied more heavily on adverse imaging features, while low-risk patients demonstrated balanced modality contributions. Risk stratification identified clinically meaningful groups with 4-fold (GBM) to 5-fold (NSCLC) differences in median survival, directly informing treatment intensity decisions. By combining state-of-the-art performance with clinical interpretability, EAGLE bridges the gap between advanced AI capabilities and practical healthcare deployment, offering a scalable solution for multimodal survival prediction that enhances both prognostic accuracy and physician trust in automated predictions.

Early detection of precancerous cysts or neoplasms, i.e., Intraductal Papillary Mucosal Neoplasms (IPMN), in pancreas is a challenging and complex task, and it may lead to a more favourable outcome. Once detected, grading IPMNs accurately is also necessary, since low-risk IPMNs can be under surveillance program, while high-risk IPMNs have to be surgically resected before they turn into cancer. Current standards (Fukuoka and others) for IPMN classification show significant intra- and inter-operator variability, beside being error-prone, making a proper diagnosis unreliable. The established progress in artificial intelligence, through the deep learning paradigm, may provide a key tool for an effective support to medical decision for pancreatic cancer. In this work, we follow this trend, by proposing a novel AI-based IPMN classifier that leverages the recent success of transformer networks in generalizing across a wide variety of tasks, including vision ones. We specifically show that our transformer-based model exploits pre-training better than standard convolutional neural networks, thus supporting the sought architectural universalism of transformers in vision, including the medical image domain and it allows for a better interpretation of the obtained results.

Intraductal papillary mucinous neoplasms (IPMNs) are precursor lesions of pancreatic adenocarcinoma. Artificial intelligence (AI) is a mathematical concept whose implementation automates learning and recognizing data patterns. The aim of this study was to investigate whether AI via deep learning algorithms using endoscopic ultrasonography (EUS) images of IPMNs could predict malignancy.

Abstract--Cancer is among the leading causes of death worldwide. Risk stratification of cancer tumors in radiology images can be improved with computer-aided diagnosis (CAD) tools which can be made faster and more accurate. Tumor characterization through CADs can enable noninvasive cancer staging and prognosis, and foster personalized treatment planning as a part of precision medicine. In this study, we propose both supervised and unsupervised machine learning strategies to improve tumor characterization. Our first approach is based on supervised learning for which we demonstrate significant gains in deep learning algorithms, particularly by utilizing a 3D Convolutional Neural Network along with transfer learning. Motivated by the radiologists' interpretations of the scans, we then show how to incorporate task dependent feature representations into a CAD system via a "graph-regularized sparse Multi-Task Learning (MTL)" framework. In the second approach, we explore an unsupervised scheme to address the limited availability of labeled training data, a common problem in medical imaging applications. Inspired by learning from label proportion (LLP) approaches, we propose a new algorithm, proportion-SVM, to characterize tumor types. We also seek the answer to the fundamental question about the goodness of "deep features" for unsupervised tumor classification. We evaluate our proposed approaches (both supervised and unsupervised) on two different tumor diagnosis challenges: lung and pancreas with 1018 CT and 171 MRI scans respectively. Cancer affects a significant number of the world's population; approximately 40% of people will be diagnosed with cancer at some point during their lifetime with an overall mortality of 171.2 per 100,000 people per year (based on 2008-2012 deaths) [1]. Lung and pancreatic cancers are two of the most common cancers. While lung cancer is the largest cause of cancer-related deaths in the world, pancreatic cancer has the poorest prognosis with a 5-year survival rate of just 7% in the United States [1]. With regards to pancreatic cancer, specifically in this work, we focus on the challenging problem of automatic diagnosis of Intraductal Papillary Mucinous Neoplasms (IPMN). IPMN is a pre-malignant condition and if left untreated, it can progress to invasive cancer.

Pancreatic cancer has the poorest prognosis among all cancer types. Intraductal Papillary Mucinous Neoplasms (IPMNs) are radiographically identifiable precursors to pancreatic cancer; hence, early detection and precise risk assessment of IPMN are vital. In this work, we propose a Convolutional Neural Network (CNN) based computer aided diagnosis (CAD) system to perform IPMN diagnosis and risk assessment by utilizing multi-modal MRI. In our proposed approach, we use minimum and maximum intensity projections to ease the annotation variations among different slices and type of MRIs. Then, we present a CNN to obtain deep feature representation corresponding to each MRI modality (T1-weighted and T2-weighted). At the final step, we employ canonical correlation analysis (CCA) to perform a fusion operation at the feature level, leading to discriminative canonical correlation features. Extracted features are used for classification. Our results indicate significant improvements over other potential approaches to solve this important problem. The proposed approach doesn't require explicit sample balancing in cases of imbalance between positive and negative examples. To the best of our knowledge, our study is the first to automatically diagnose IPMN using multi-modal MRI.