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How Many Events Make an Object? Improving Single-frame Object Detection on the 1 Mpx Dataset

• Authors : Kugele, Alexander; Pfeil, Thomas; Pfeiffer, Michael

• Source: Kugele, A, Pfeil, T, Pfeiffer, M & Chicca, E 2023, How Many Events Make an Object? Improving Single-frame Object Detection on the 1 Mpx Dataset. in Proceedings - 2023 IEEE/CVF Conference on Computer

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How Many Events Make an Object? Improving Single-frame Object Detection on the 1 Mpx Dataset

• Authors : Kugele, Alexander; Pfeil, Thomas; Pfeiffer, Michael

• Source: Kugele, A, Pfeil, T, Pfeiffer, M & Chicca, E 2023, How Many Events Make an Object? Improving Single-frame Object Detection on the 1 Mpx Dataset. in Proceedings - 2023 IEEE/CVF Conference on Computer

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Fully Automated Thrombus Segmentation on CT Images of Patients with Acute Ischemic Stroke

• Authors : Mojtahedi, Mahsa; Kappelhof, Manon; Ponomareva, Elena

• Source: Mojtahedi, M, Kappelhof, M, Ponomareva, E, Tolhuisen, M, Jansen, I, Bruggeman, A A E, Dutra, B G, Yo, L, LeCouffe, N, Hoving, J W, van Voorst, H, Brouwer, J, Terreros, N A, Konduri, P, Meijer, F J A,

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Fully Automated Thrombus Segmentation on CT Images of Patients with Acute Ischemic Stroke

• Authors : Mojtahedi, Mahsa; Kappelhof, Manon; Ponomareva, Elena

• Source: Mojtahedi, M, Kappelhof, M, Ponomareva, E, Tolhuisen, M, Jansen, I, Bruggeman, A A E, Dutra, B G, Yo, L, LeCouffe, N, Hoving, J W, van Voorst, H, Brouwer, J, Terreros, N A, Konduri, P, Meijer, F J A,

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Deep Radiogenomics Sequencing for Breast Tumor Gene-Phenotype Decoding Using Dynamic Contrast Magnetic Resonance Imaging

• Authors : Shiri, Isaac; Salimi, Yazdan; Mohammadi Kazaj, Pooya

Subjects: Breast; Deep learning; Estrogen receptors

• Source: Shiri, I, Salimi, Y, Mohammadi Kazaj, P, Bagherieh, S, Amini, M, Saberi Manesh, A & Zaidi, H 2025, 'Deep Radiogenomics Sequencing for Breast Tumor Gene-Phenotype Decoding Using Dynamic Contrast

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Deep Radiogenomics Sequencing for Breast Tumor Gene-Phenotype Decoding Using Dynamic Contrast Magnetic Resonance Imaging

• Authors : Shiri, Isaac; Salimi, Yazdan; Mohammadi Kazaj, Pooya

Subjects: Breast; Deep learning; Estrogen receptors

• Source: Shiri, I, Salimi, Y, Mohammadi Kazaj, P, Bagherieh, S, Amini, M, Saberi Manesh, A & Zaidi, H 2025, 'Deep Radiogenomics Sequencing for Breast Tumor Gene-Phenotype Decoding Using Dynamic Contrast

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Swin UNETR for Tumor and Lymph Node Segmentation Using 3D PET/CT Imaging:A Transfer Learning Approach

• Authors : Chu, Hung; De la O Arévalo, Luis Ricardo; Tang, Wei ...

Subjects: Auto contouring; Deep learning; Head and neck cancer

• Source: Chu, H, De la O Arévalo, L R, Tang, W, Ma, B, Li, Y, De Biase, A, Both, S, Langendijk, J A, van Ooijen, P, Sijtsema, N M & van Dijk, L V 2023, Swin UNETR for Tumor and Lymph Node Segmentation Using

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Development and external validation of automated detection, classification, and localization of ankle fractures:inside the black box of a convolutional neural network (CNN)

• Authors : Prijs, Jasper; Liao, Zhibin; To, Minh Son

Subjects: Ankle; Artificial Intelligence; CNN

• Source: Machine Learning Consortium, Prijs, J, Liao, Z, To, M S, Verjans, J, Jutte, P C, Stirler, V, Olczak, J, Gordon, M, Guss, D, DiGiovanni, C W, Jaarsma, R L, IJpma, F F A & Doornberg, J N 2023,

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Sanity Checks for Saliency Methods Explaining Object Detectors

• Authors : Padmanabhan, Deepan Chakravarthi; Plöger, Paul G.; Arriaga, Octavio ...

Subjects: Object detectors; Saliency methods; Sanity checks

• Source: Padmanabhan, D C, Plöger, P G, Arriaga, O & Valdenegro-Toro, M 2023, Sanity Checks for Saliency Methods Explaining Object Detectors. in L Longo (ed.), Explainable Artificial Intelligence : First

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Fully automated accurate patient positioning in computed tomography using anterior–posterior localizer images and a deep neural network:a dual-center study

• Authors : Salimi, Yazdan; Shiri, Isaac; Akavanallaf, Azadeh

Subjects: Computed tomography; CT localizer; Deep learning

• Source: Salimi, Y, Shiri, I, Akavanallaf, A, Mansouri, Z, Arabi, H & Zaidi, H 2023, 'Fully automated accurate patient positioning in computed tomography using anterior–posterior localizer images and a deep

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