Jun 17, 2020 · Abstract:Instance level detection of thoracic diseases or abnormalities are crucial for automatic diagnosis in chest X-ray images.

We provide a new benchmark called ChestX-Det10, including box-level annotations of 10 categories of disease/abnormality of $\sim$ 3,500 images. The annotations ...

We select 3,543 images from NIH ChestX-14 and invite three board-certified radiologists to annotate them with 10 common categories of diseases or abnormalities.

A new benchmark called ChestX-det10 is provided, including box-level annotations of 10 categories of disease/abnormality of 3,500 chest X-ray images, ...

We provide a new benchmark called. Chest X-Det10, including box-level annotations of 10 categories of dis- ease/abnormality of ~ 3,500 images. The annotations ...

We provide a new benchmark called ChestX-det10, including box-level annotations of 10 categories of disease/abnormality of $\sim$ 3,500 images. The annotations ...

The detection of thoracic abnormalities challenge is organized by the Deepwise AI Lab and ChestX-Det10 is the first chest X-Ray dataset with instance-level ...

In this paper, we present the results of 6 teams which reach the second round. The challenge adopts the ChestX-Det10 dateset proposed by the Deepwise AI Lab.

Jul 28, 2023 · ChestX-Det10 is a subset of NIH ChestXray14, which consists of 3543 chest X-ray images with box-level annotations provided by three board- ...

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