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All Journal CommIT (Communication & Information Technology) Medicinus : Jurnal Kedokteran
Harsono, Ivan William
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Computer Science & IT Medicine & Pharmacology Public Health

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Lung Nodule Texture Detection and Classification Using 3D CNN Harsono, Ivan William
CommIT (Communication and Information Technology) Journal Vol 13, No 2 (2019): CommIT Vol. 13 No. 2 Tahun 2019
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v13i2.5995

Abstract

Following artificial intelligence implementation in computer vision field, especially deep learning, many Computer-Aided Diagnosis (CAD) tools are proposed to help to detect lung cancer by the scoring system or by identifying the characteristics of nodules. However, lung cancer is a clinical diagnosis which does not provide detailed information needed by radiologists and clinician to prevent unnecessary invasive diagnostic procedures compared to lung nodule texture detection and classification. Hence, to answer this problem, this research explores the steps needed to implement 3D CNN on raw thorax CT scan datasets and usage of RetinaNet 3D + Inception 3D with transfer learning. The 3D CNN CAD tools can improve the speed, performance, and ability to detect lung nodule texture instead of malignancy status done by previous studies. This research implements 3D CNN on Moscow private datasets acquired from NVIDIA Asia Pacific. The proposed method of data conversion can minimize information loss from raw data to 3D CNN input data. On training phase, after 100 epochs, the researchers conclude that the best-proposed model (3D CNN with transfer learning of pretrained LIDC public datasets weight) provides 22.86% of mean average precision (mAP) detection capability and 70.36% of Area Under the Curve (AUC) in Moscow private dataset lung texture detection tasks. It outperforms non-transfer learning 3D CNN model (trained from scratch) and 3D CNN with transfer learning of pre-trained ImageNet weight.
The Importance of Immunohistochemical Analysis in Silent Pituitary Adenoma Harsono, Ivan William; Stevina, Nathania Victoria; Puspitasari, Vivien; July, Julius
Medicinus Vol. 6 No. 3 (2017): June 2017 - September 2017
Publisher : Fakultas Kedokteran Universitas Pelita Harapan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19166/med.v6i3.1150

Abstract

Pituitary adenoma contributes to 15% of all intracranial neoplasm. It is usually following benign course and some of them are silent (asymptomatic clinically, but hormone-secreting). Silent adenoma usually found incidentally or when the patients show mass effect (neurological deficits). Many of histologically aggressive silent adenoma subtypes are associated with invasiveness, recurrence and progression to clinically functioning adenomas. Aggressive silent adenoma radiologically tends to invade in downward direction, invading bone, sinus cavernosus, parasellar region. The nature of aggressive silent adenoma subtypes is differing in nature compared to benign nature of pituitary adenoma and should be confirmed immunohistochemically to determine the prognosis and anticipate the risk of recurrence or progression. The case illustration show a real case of 46 years old female progressive headache and visual disturbance diagnosed with non-functional pituitary macroadenoma but positive for more than one immunochemistry biomarker (plurihormonal aggressive silent adenoma).
Co-Authors Julius July Stevina, Nathania Victoria Vivien Puspitasari, Vivien