Self-Supervised Learning for Spinal MRIs

A significant proportion of patients scanned in a clinical setting have follow-up scans. We show in this work that such longitudinal scans alone can be used as a form of 'free' self-supervision for training a deep network. We demonstrate this self-supervised learning for the case of T2-weighted sagittal lumbar Magnetic Resonance Images (MRIs). A Siamese convolutional neural network (CNN) is trained using two losses: (i) a contrastive loss on whether the scan is of the same person (i.e. longitudinal) or not, together with (ii) a classification loss on predicting the level of vertebral bodies. The performance of this pre-trained network is then assessed on a grading classification task. We experiment on a dataset of 1016 subjects, 423 possessing follow-up scans, with the end goal of learning the disc degeneration radiological gradings attached to the intervertebral discs. We show that the performance of the pre-trained CNN on the supervised classification task is (i) superior to that of a network trained from scratch; and (ii) requires far fewer annotated training samples to reach an equivalent performance to that of the network trained from scratch.Comment: 3rd Workshop on Deep Learning in Medical Image Analysi

Morfologi Nomina dan Adjektiva Bahasa Totoli

Bibliografi hlm. 84103 hlm. ;21 cm

Sastra Lisan Wolio

-xi, 85 hlm. ; 21 cm

Struktur sastra lisan Mori

Buku Struktur Sastra Lisan Mori ini merupakan salah satu hasil Proyek Penelitian Bahasa dan Sastra Indonesia dan Daerah Sulawesi Tengah tahun 1992 yang pelaksanaannya dipercayakan kepada tim peneliti dari Kecamatan Lembo. Untuk itu, kami ingin menyatakan penghargaan dan ucapan terima kasih kepada Pemimpin Proyek Penelitian Bahasa dan Sastra Indonesia dan Daerah Sulawesi Tengah beserta stafnya, dan para peneliti, yaitu Tim Peneliti Drs. Ahmad Saro, Drs. Amir Kadir, Drs. llyas Abd. Hamid

Vision-Language Modelling For Radiological Imaging and Reports In The Low Data Regime

This paper explores training medical vision-language models (VLMs) -- where the visual and language inputs are embedded into a common space -- with a particular focus on scenarios where training data is limited, as is often the case in clinical datasets. We explore several candidate methods to improve low-data performance, including: (i) adapting generic pre-trained models to novel image and text domains (i.e. medical imaging and reports) via unimodal self-supervision; (ii) using local (e.g. GLoRIA) & global (e.g. InfoNCE) contrastive loss functions as well as a combination of the two; (iii) extra supervision during VLM training, via: (a) image- and text-only self-supervision, and (b) creating additional positive image-text pairs for training through augmentation and nearest-neighbour search. Using text-to-image retrieval as a benchmark, we evaluate the performance of these methods with variable sized training datasets of paired chest X-rays and radiological reports. Combined, they significantly improve retrieval compared to fine-tuning CLIP, roughly equivalent to training with the data. A similar pattern is found in the downstream task classification of CXR-related conditions with our method outperforming CLIP and also BioVIL, a strong CXR VLM benchmark, in the zero-shot and linear probing settings. We conclude with a set of recommendations for researchers aiming to train vision-language models on other medical imaging modalities when training data is scarce. To facilitate further research, we will make our code and models publicly available.Comment: Accepted to MIDL 202

PENGARUH KEPEMIMPINAN, KOMPENSASI, DAN KEDISIPLINAN TERHADAP KINERJA TENAGA MAGANG DI PUSKESMAS MANGKOSO KABUPATEN BARRU

ABSTRAKPenelitian ini dilaksanakan pada Puskesmas Mangkoso Kabupaten Barru. Sampel menggunakan metode total sampling, yaitu semua anggota populasi digunakan sebagai sampel. Semua sampel berjumlah 30 responden pada Puskesmas Mangkoso Kabupaten Barru. Metode pengumpulan data yang digunakan adalah Wawancara langsung kepada pihak – pihak yang terlibat dengan masalah yang sedang dibahas serta memberikan kuesioner kepada pegawai yang sesuai dengan penelitian yang dilakukan. Analisis data dilakukan dengan menggunakan analisa regresi linear berganda, Uji T-test, Uji F serta Uji Koefisien Determinasi (R2). Hasil analisis menunjukan bahwa (1) kepemimpinan berpengaruh terhadap kinerja tenaga magang Di Puskesmas Mangkoso Kabupaten Barru (2) kompensasi berpengaruh terhadap kinerja tenaga magang Di Puskesmas Mangkoso Kabupaten Barru (3) kedisiplinanberpengaruh terhadap kinerja tenaga magang Di Puskesmas Mangkoso Kabupaten Barru (4) kepemimpinan, kompensasi, dan kedisiplinan berpengaruh terhadap kinerja tenaga magang Di Puskesmas Mangkoso Kabupaten Barru Kata Kunci: Kepemimpinan, Kompensasi, Kedisiplinan, dan Kinerja Tenaga Magang  ABSTRACTThis research was carried out at Mangkoso Public Health Center in Barru Regency. The sample uses a total sampling method, that is, all members of the population are used as samples. All samples were 30 respondents at Mangkoso Health Center, Barru District. Data collection methods used were direct interviews with parties involved with the issues being discussed and giving questionnaires to employees in accordance with the research conducted. Data analysis was performed using multiple linear regression analysis, T-test, F test and Determination Coefficient Test (R2). The results of the analysis show that (1) leadership influences the performance of apprentices at Mangkoso Public Health Center in Barru District (2) compensation influences the performance of apprentices at Mangkoso Health Center Barru District (3) discipline influences the performance of apprentices at Mangkoso Health Center Barru District (4) leadership, compensation, and discipline influences the performance of apprentices at Mangkoso Health Center Barru District Keywords: Leadership, Compensation, Discipline, and Internship Performanc

Empirical analysis of rough set categorical clustering techniques based on rough purity and value set

Clustering a set of objects into homogeneous groups is a fundamental operation in data mining. Recently, attention has been put on categorical data clustering, where data objects are made up of non-numerical attributes. The implementation of several existing categorical clustering techniques is challenging as some are unable to handle uncertainty and others have stability issues. In the process of dealing with categorical data and handling uncertainty, the rough set theory has become well-established mechanism in a wide variety of applications including databases. The recent techniques such as Information-Theoretic Dependency Roughness (ITDR), Maximum Dependency Attribute (MDA) and Maximum Significance Attribute (MSA) outperformed their predecessor approaches like Bi-Clustering (BC), Total Roughness (TR), Min-Min Roughness (MMR), and standard-deviation roughness (SDR). This work explores the limitations and issues of ITDR, MDA and MSA techniques on data sets where these techniques fails to select or faces difficulty in selecting their best clustering attribute. Accordingly, two alternative techniques named Rough Purity Approach (RPA) and Maximum Value Attribute (MVA) are proposed. The novelty of both proposed approaches is that, the RPA presents a new uncertainty definition based on purity of rough relational data base whereas, the MVA unlike other rough set theory techniques uses the domain knowledge such as value set combined with number of clusters (NoC). To show the significance, mathematical and theoretical basis for proposed approaches, several propositions are illustrated. Moreover, the recent rough categorical techniques like MDA, MSA, ITDR and classical clustering technique like simple K-mean are used for comparison and the results are presented in tabular and graphical forms. For experiments, data sets from previously utilized research cases, a real supply base management (SBM) data set and UCI repository are utilized. The results reveal significant improvement by proposed techniques for categorical clustering in terms of purity (21%), entropy (9%), accuracy (16%), rough accuracy (11%), iterations (99%) and time (93%). vi