Titlebook: Diabetic Foot Ulcers Grand Challenge; Third Challenge, DFU Moi Hoon Yap,Connah Kendrick,Bill Cassidy Conference proceedings 2023 The Editor

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On the?Optimal Combination of?Cross-Entropy and?Soft Dice Losses for?Lesion Segmentation with?Out-ofo its ability to generalize to OoD data, via comprehensive experiments on polyp segmentation from endoscopic images and ulcer segmentation from diabetic feet images. Our findings are surprising: CE-Dice loss combinations that excel in segmenting in-distribution images have a poor performance when de

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DFU-Ens: End-to-End Diabetic Foot Ulcer Segmentation Framework with?Vision Transformer Based Detectiding-box detection (performed using the latest DETR vision transformer architecture and YOLOv4) and patch segmentation. On the DFUC2022 validation set, we achieved 0.643 Dice score for the ensemble approach, 0.648 for DFU-Seg, and 0.556 and 0.581 for hybrid approaches based on YOLOv4 and DETR, respe

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https://doi.org/10.1007/978-3-540-76432-8o its ability to generalize to OoD data, via comprehensive experiments on polyp segmentation from endoscopic images and ulcer segmentation from diabetic feet images. Our findings are surprising: CE-Dice loss combinations that excel in segmenting in-distribution images have a poor performance when de

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https://doi.org/10.1007/978-3-662-57878-0ding-box detection (performed using the latest DETR vision transformer architecture and YOLOv4) and patch segmentation. On the DFUC2022 validation set, we achieved 0.643 Dice score for the ensemble approach, 0.648 for DFU-Seg, and 0.556 and 0.581 for hybrid approaches based on YOLOv4 and DETR, respe

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,Quantifying the?Effect of?Image Similarity on?Diabetic Foot Ulcer Classification, deep learning classification networks. The presence of binary-identical duplicate images in datasets used to train deep learning algorithms is a well known issue that can introduce unwanted bias which can degrade network performance. However, the effect of visually similar non-identical images is a

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