Negation is Not Semantic: Diagnosing Dense Retrieval Failure Modes for Trade-offs in Contradiction-Aware Biomedical QA

Large Language Models (LLMs) have demonstrated strong capabilities in biomedical question answering, yet their tendency to generate plausible but unverified claims poses serious risks in clinical settings. To mitigate these risks, the TREC 2025 BioGen track mandates grounded answers that explicitly surface contradictory evidence (Task A) and the generation of narrative driven, fully attributed responses (Task B). Addressing the absence of target ground truth, we present a proxy-based development framework using the SciFact dataset to systematically optimize retrieval architectures. Our iterative evaluation revealed a "Simplicity Paradox": complex adversarial dense retrieval strategies failed catastrophically at contradiction detection (MRR 0.023) due to Semantic Collapse, where negation signals become indistinguishable in vector space. We further identify a Retrieval Asymmetry: filtering dense embeddings improves contradiction detection but degrades support recall, compromising reliability. We resolve this via a Decoupled Lexical Architecture built on a unified BM25 backbone, balancing semantic support recall (0.810) with precise contradiction surfacing (0.750). This approach achieves the highest Weighted MRR (0.790) on the proxy benchmark while remaining the only viable strategy for scaling to the 30 million document PubMed corpus. For answer generation, we introduce Narrative Aware Reranking and One-Shot In-Context Learning, improving citation coverage from 50% (zero-shot) to 100%. Official TREC results confirm our findings: our system ranks 2nd on Task A contradiction F1 and 3rd out of 50 runs on Task B citation coverage (98.77%), achieving zero citation contradict rate. Our work transforms LLMs from stochastic generators into honest evidence synthesizers, showing that epistemic integrity in biomedical AI requires precision and architectural scalability isolated metric optimization.

AAD-DCE: An Aggregated Multimodal Attention Mechanism for Early and Late Dynamic Contrast Enhanced Prostate MRI Synthesis

Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is a medical imaging technique that plays a crucial role in the detailed visualization and identification of tissue perfusion in abnormal lesions and radiological suggestions for biopsy. However, DCE-MRI involves the administration of a Gadolinium based (Gad) contrast agent, which is associated with a risk of toxicity in the body. Previous deep learning approaches that synthesize DCE-MR images employ unimodal non-contrast or low-dose contrast MRI images lacking focus on the local perfusion information within the anatomy of interest. We propose AAD-DCE, a generative adversarial network (GAN) with an aggregated attention discriminator module consisting of global and local discriminators. The discriminators provide a spatial embedded attention map to drive the generator to synthesize early and late response DCE-MRI images. Our method employs multimodal inputs - T2 weighted (T2W), Apparent Diffusion Coefficient (ADC), and T1 pre-contrast for image synthesis. Extensive comparative and ablation studies on the ProstateX dataset show that our model (i) is agnostic to various generator benchmarks and (ii) outperforms other DCE-MRI synthesis approaches with improvement margins of +0.64 dB PSNR, +0.0518 SSIM, -0.015 MAE for early response and +0.1 dB PSNR, +0.0424 SSIM, -0.021 MAE for late response, and (ii) emphasize the importance of attention ensembling. Our code is available at https://github.com/bhartidivya/AAD-DCE.