RESUMO
Background: Accurate semantic segmentation of kidney tumors in computed tomography (CT) images is difficult because tumors feature varied forms and occasionally, look alike. The KiTs19 challenge sets the groundwork for future advances in kidney tumor segmentation. Methods: We present weight pruning (WP)-UNet, a deep network model that is lightweight with a small scale; it involves few parameters with a quick assumption time and a low floating-point computational complexity. Results: We trained and evaluated the model with CT images from 210 patients. The findings implied the dominance of our method on the training Dice score (0.98) for the kidney tumor region. The proposed model only uses 1,297,441 parameters and 7.2e floating-point operations, three times lower than those for other network models. Conclusions: The results confirm that the proposed architecture is smaller than that of UNet, involves less computational complexity, and yields good accuracy, indicating its potential applicability in kidney tumor imaging.
RESUMO
Sarcoidosis is a systemic disease characterized by non caseating granulomatous inflammation. Sarcoidosis can affect any organ, but it most commonly involves the lungs and lymph nodes. The exact incidence of renal involvement in sarcoidosis remains unclear, but it is found to be rare. Granulomatous interstitial nephritis is the most common histological pattern, but its presentation with renal insufficiency is rare. Here, we present a case of sarcoidosis causing granulomatous interstitial nephritis presenting as isolated non oliguric acute renal failure.
