Tungjitkusolmun, Supan
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Tungjitkusolmun, Supan
Alternative Name
Tungjitkusolmun, S.
Main Affiliation
Email
supan.tu@kmitl.ac.th
24 results
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Item type:Publication, Mechanical compliance of the endocardium(2002-12-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Cross-Sensor and Cross-Population Generalization of Deep Learning Models for Digital Mammography: A Controlled Four-Country Benchmark of Five Backbone Architectures with Statistical Significance Testing(2026-06-01); Background/Objectives: Deep learning models for digital mammography sensor data are increasingly deployed across hospitals using different X-ray detector technologies and patient populations. Whether models trained on one sensor platform and population maintain accuracy when transferred to another has not been tested for the latest generation of mammography-specific foundation models under one controlled protocol. Methods: We fine-tuned five backbone architectures (ResNet-50, DINOv2-B14, Rad-DINO, Mammo-CLIP B5, and Mammo-FM) on CBIS-DDSM (film-digitized, USA, n = 714 validation) with three seeds, ablated a density-aware focal loss across three auxiliary weights, and evaluated transfer to three external sensor cohorts: CMMD (full-field digital, China, n = 1032), DMID (mixed digital, India, n = 509), and MIAS (film-digitized, UK, n = 322). Significance used paired DeLong z-tests with Benjamini–Hochberg FDR correction; temperature scaling tested post hoc recalibration at all transfer targets. Results: Within this single-source three-seed evaluation, ResNet-50 outperformed all four foundation models on CBIS-DDSM (AUC 0.867 vs. 0.847, 0.846, 0.813, and 0.703; all gaps p_adj < 0.05). The density-aware focal loss degraded both AUC and calibration at every weight tested. At transfer, every model lost 0.165 to 0.320 AUC points relative to in-distribution performance, with sensitivity at 95% specificity collapsing from 0.31 to 0.47 in-distribution to 0.11 to 0.22 across the three external targets. A per-seed Stouffer meta-analysis confirms that Mammo-CLIP B5 and Mammo-FM significantly outperformed ResNet-50 on DMID and Mammo-CLIP on CMMD, after BH-FDR; MIAS comparisons remained directional only. In the extremely dense subgroup (BI-RADS D4), Mammo-FM reached AUC 0.870 versus ResNet-50 at 0.842, a directional observation whose 95% CIs overlap heavily at the n = 140 sample size and which we do not interpret as a statistically supported advantage. Conclusions: In this single training-source, three-seed protocol, mammography-specific pretraining did not deliver the in-distribution AUC premium reported in the originating papers, and no architecture reached a level at which transfer deployment without local validation would be defensible. We frame these as observations specific to the present protocol rather than as broader conclusions about foundation models for mammography classification. The findings argue for sensor-stratified and population-stratified external validation and for local recalibration as practical prerequisites before clinical use. Code and weights are released under MIT license. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Error analysis of tissue resistivity measurement(2002-04-30) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Automated Crack Detection in Monolithic Zirconia Crowns Using Acoustic Emission and Deep Learning Techniques(2024-09-01); ; Monolithic zirconia (MZ) crowns are widely utilized in dental restorations, particularly for substantial tooth structure loss. Inspection, tactile, and radiographic examinations can be time-consuming and error-prone, which may delay diagnosis. Consequently, an objective, automatic, and reliable process is required for identifying dental crown defects. This study aimed to explore the potential of transforming acoustic emission (AE) signals to continuous wavelet transform (CWT), combined with Conventional Neural Network (CNN) to assist in crack detection. A new CNN image segmentation model, based on multi-class semantic segmentation using Inception-ResNet-v2, was developed. Real-time detection of AE signals under loads, which induce cracking, provided significant insights into crack formation in MZ crowns. Pencil lead breaking (PLB) was used to simulate crack propagation. The CWT and CNN models were used to automate the crack classification process. The Inception-ResNet-v2 architecture with transfer learning categorized the cracks in MZ crowns into five groups: labial, palatal, incisal, left, and right. After 2000 epochs, with a learning rate of 0.0001, the model achieved an accuracy of 99.4667%, demonstrating that deep learning significantly improved the localization of cracks in MZ crowns. This development can potentially aid dentists in clinical decision-making by facilitating the early detection and prevention of crack failures. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Patient-specific aided surgery approach of deviated nasal septum using computational fluid dynamics(2015-05-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Three-dimensional finite-element analyses for radio-frequency hepatic tumor ablation(2002-01-05); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Automated segmentation of infarct lesions in t1‐weighted mri scans using variational mode decomposition and deep learning(2021-03-02); ; ; Automated segmentation methods are critical for early detection, prompt actions, and immediate treatments in reducing disability and death risks of brain infarction. This paper aims to develop a fully automated method to segment the infarct lesions from T1‐weighted brain scans. As a key novelty, the proposed method combines variational mode decomposition and deep learning-based segmentation to take advantages of both methods and provide better results. There are three main technical contributions in this paper. First, variational mode decomposition is applied as a pre-processing to discriminate the infarct lesions from unwanted non‐infarct tissues. Second, overlapped patches strategy is proposed to reduce the workload of the deep‐learning‐based segmentation task. Finally, a three‐dimensional U‐Net model is developed to perform patch‐wise segmentation of infarct lesions. A total of 239 brain scans from a public dataset are utilized to develop and evaluate the proposed method. Empirical results reveal that the proposed automated segmentation can provide promising performances with an average dice similarity coefficient (DSC) of 0.6684, intersection over union (IoU) of 0.5022, and average symmetric surface distance (ASSD) of 0.3932, respectively. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Automatic detection of pulmonary nodules using three-dimensional chain coding and optimized random forest(2020-04-01); ; ; The detection of pulmonary nodules on computed tomography scans provides a clue for the early diagnosis of lung cancer. Manual detection mandates a heavy radiological workload as it identifies nodules slice-by-slice. This paper presents a fully automated nodule detection with three significant contributions. First, an automated seeded region growing is designed to segment the lung regions from the tomography scans. Second, a three-dimensional chain code algorithm is implemented to refine the border of the segmented lungs. Lastly, nodules inside the lungs are detected using an optimized random forest classifier. The experiments for our proposed detection are conducted using 888 scans from a public dataset, and achieves a favorable result of 93.11% accuracy, 94.86% sensitivity, and 91.37% specificity, with only 0.0863 false positives per exam. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, ECCCII-based current-mode universal filter with orthogonal control of ωo and Q(2014-01-01); ; ; Junnapiya, SomyotThis paper presents a new current-mode current- controlled four-input five-output universal filter employing one current-controlled current conveyor (CCCII), one electronically tunable CCCII and two grounded capacitors. The proposed configuration provides lowpass, bandpass, highpass, bandstop and allpass current responses that taken from the high-output impedance terminals, which enable easy cascadability of the current-mode operation. The filter also offers both orthogonal and electronic controls of the natural frequency and the quality factor through adjusting the bias current of the CCCIIs. For realizing all the filter responses, the proposed filter does not require passive component-matching condition and both active and passive sensitivities are low. In addition, a new current-mode current-controlled single-input five-output universal filter can be achieved by using an additional multiple-output minus-type CCCII. The proposed filter is simulated using PSPICE simulations to confirm the theoretical analysis. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Modeling bipolar phase-shifted multielectrode catheter ablation(2002-01-05);