Abstract #2028

Suitability of Deep Weakly-supervised Learning to Detect Acute Ischemic Strokes and Hemorrhagic Infarctions Using Diffusion-weighted Imaging

Chen Cao^1,2, Zhiyang Liu³, Guohua Liu³, Jinxia Zhu⁴, Song Jin¹, and Shuang Xia⁵

¹Key Laboratory for Cerebral Artery and Neural Degeneration of Tianjin, Radiology Department, Tianjin Huanhu Hospital, Tianjin, China, ²Radiology Department, First Central Clinical College, Tianjin Medical University, Tianjin, China, ³Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology, College of Electronic Information and Optical Engineering, Nankai University, Tianjin, China, ⁴MR Collaboration, Siemens Healthcare Ltd, Beijing, China, ⁵Radiology Department, Tianjin First Central Hospital, Tianjin, China

We hypothesized that deep weakly-supervised learning could detect acute ischemic stroke (AIS) and hemorrhagic infarction (HI) lesions using diffusion-weighted imaging. Each image slice was assigned an annotation indicating whether or not the slice contained a lesion. The proposed method was trained on an AIS dataset using 417 patients with weakly-labeled lesions and evaluated on a dataset using 319 patients with fully-labeled lesions, which detected lesions with high accuracy. The method was trained on a HI dataset using 240 patients with weakly-labeled lesions and evaluated using 65 patients with fully-labeled lesions. Lesion detection sensitivities were 87.7% (AISs) and 86.2% (HIs).

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