# MApLe: Multi-instance Alignment of Diagnostic Reports and Large Medical Images Source: [MApLe: Multi-instance Alignment of Diagnostic Reports and Large Medical Images]() Collection index: [llms.txt]() Data updated: 2026-04-17T05:00:13.532Z Authors: Felicia Bader, Philipp Seeb\\"ock, Anastasia Bartashova, Ulrike Attenberger, Georg Langs Published: 2026-04-15T15:19:54.000Z ## Abstract In diagnostic reports, experts encode complex imaging data into clinically actionable information. They describe subtle pathological findings that are meaningful in their anatomical context. Reports follow relatively consistent structures, expressing diagnostic information with few words that are often associated with tiny but consequential image observations. Standard vision language models struggle to identify the associations between these informative text components and small locations in the images. Here, we propose "MApLe", a multi-task, multi-instance vision language alignment approach that overcomes these limitations. It disentangles the concepts of anatomical region and diagnostic finding, and links local image information to sentences in a patch-wise approach. Our method consists of a text embedding trained to capture anatomical and diagnostic concepts in sentences, a patch-wise image encoder conditioned on anatomical structures, and a multi-instance alignment of these representations. We demonstrate that MApLe can successfully align different image regions and multiple diagnostic findings in free-text reports. We show that our model improves the alignment performance compared to state-of-the-art baseline models when evaluated on several downstream tasks. The code is available at https://github.com/cirmuw/MApLe. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Calcification Classification on internal 3D heart CT dataset](): Calcification Classification; internal 3D heart CT dataset; Accuracy: 57.31. - [Stenosis Classification on internal 3D heart CT dataset](): Stenosis Classification; internal 3D heart CT dataset; Accuracy: 75.77. ## Original sources and implementation [Original paper]() [PDF]()