18 DAILY MICCAI Monday Poster Presentation methods. But the results get much better when it’s known which anatomic regions the classified pathologies belong to. While this seems like a lot of work, it actually isn’t because when you have the radiology reports, you can automatically extract where those belong in some semi-automated way with some rules.” He is keen to point out that the groundwork for this approach, including the use of anatomic regions and information extraction from radiology reports, has been laid by others. The Chest ImaGenome dataset, derived from the MIMIC-CXR dataset, serves as a valuable foundation for this innovative work. Regarding the next steps, while the current methodology relies on exact anatomical regions for localization, there are inherent limitations in spotting small pathologies. “While with weighted box fusion, there’s a trick to allow it to merge different bounding boxes and be a bit more precise on the localization of the pathology, there’s no way that it can spot small pathologies that only cover a very small part of the anatomic region,” Philip explains. “It’s just impossible by the design of the method! It might not be too problematic in some cases because a rough localization is still useful, but it’s a restriction.” To address this limitation, he envisions a more integrated approach, aiming to combine all the pipeline components more effectively. The process involves multiple steps, from anatomical region detection to exact anatomical region localization and pathology classification. He proposes directly using text embeddings of classes and anatomic regions as DETR (DEtection TRansformer) tokens. “I embed the name of the anatomic region using some pre-trained language model,” he explains. “Then I go, okay, please locate the anatomic region. Here, I have supervision for it, so I train it. On the other hand, here’s
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