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Journal of Technology Informatics and Engineering
Published by Universitas Sains Dan Teknologi Komputer
ISSN : 29619068     EISSN : 29618215     DOI : 10.51903
Core Subject : Science,
Computer Science & IT
Power Engineering Telecommunication Engineering Computer Engineering Control and Computer Systems Electronics Information technology Informatics Data and Software engineering Biomedical Engineering
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 172 Documents
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A Lightweight Medical Foundation Model for Cross-Modal Multi-Task Pretraining and Parameter-Efficient Few-Shot Transfer on MedMNIST Mi, Gaotian; Ye, Tong; Wood, Dan
Journal of Technology Informatics and Engineering Vol. 4 No. 3 (2025): DECEMBER | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v4i3.492

Abstract

Medical imaging has rapidly adopted pre-trained backbones, yet many transfer-learning pipelines remain expensive to train and difficult to adapt when data, compute, or privacy constraints limit full fine-tuning. We present STMedFM, a lightweight medical multi-task backbone baseline designed for fast prototyping across 2D images and 3D volumes. STMedFM uses modality-specific convolutional stems (2D and 3D) and a shared low-depth encoder, and it supports parameter-efficient transfer via Low-Rank Adaptation (LoRA) and bottleneck adapters. We pretrain STMedFM with supervised multi-task learning on four MedMNIST tasks (PathMNIST, BloodMNIST, DermaMNIST, and OrganMNIST3D) using official train/validation/test splits. We then compare (i) training from scratch, (ii) full fine-tuning from the multi-task checkpoint, and (iii) parameter-efficient fine-tuning (LoRA or adapters) that updates only a small fraction of parameters. Under a fixed compute budget (200 pretraining steps; 120 fine-tuning steps for 2D tasks; 50 steps for the 3D task), multi-task pretraining improved performance on PathMNIST (test accuracy 0.568 → 0.634; macro AUROC 0.886 → 0.914) and preserved most gains under PEFT (LoRA AUROC 0.909; Adapter AUROC 0.913) while training only 4,041–5,225 parameters versus 160,105 for full fine-tuning. For DermaMNIST, pretraining increased macro AUROC from 0.746 (Scratch, weighted) to 0.756 (Pretrain+Full), with similar AUROC under LoRA (0.760) and Adapter (0.763). In contrast, BloodMNIST and OrganMNIST3D showed mixed behavior, including cases where Scratch outperformed pretrained variants, indicating that transfer in this compact shared encoder is task-dependent and budget-sensitive. Calibration results were similarly non-monotonic: methods with better AUROC did not always achieve lower ECE. Overall, our results show that a small cross-modal multi-task model can serve as a practical MedMNIST-scale transfer baseline and that LoRA/adapters offer substantial parameter savings when task alignment is favorable. STMedFM should therefore be viewed as a lightweight supervised multi-task backbone on benchmark-scale tasks rather than a broadly general medical foundation model.
Offline Counterfactual Evaluation for Advertising and Recommendation Slot Policies: A Reproducible Study on the Open Bandit Dataset (Small) Mu, Jinyi; Ye, Tong; Patel, Priya
Journal of Technology Informatics and Engineering Vol. 4 No. 3 (2025): DECEMBER | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v4i3.500

Abstract

Offline or counterfactual evaluation is a critical capability for iterating advertising and recommender ranking strategies when online A/B testing is slow, expensive, or risky. Off-policy evaluation (OPE) estimates the expected reward of a candidate policy using logged interaction data from a different behavior policy. Still, it can suffer from high variance under poor overlap and can be misleading when the operational objective is choosing among candidate policies rather than minimizing point-estimation bias alone. This paper presents a fully reproducible empirical study of IPS, self-normalized IPS (SNIPS), doubly robust (DR), and Switch-DR estimators on the Open Bandit Dataset (OBD) small release. Using the Men and Women campaigns (10,000 logged item-impressions per campaign and behavior policy) collected by uniform random and Bernoulli Thompson Sampling (BTS), we construct a held-out oracle for stationary slot-wise policies from the random-traffic split and evaluate both value estimation and policy-ranking consistency on random-logged and BTS-logged test sets. Across 1,000 nonparametric bootstrap replications, IPS and SNIPS are accurate on randomly logged data, whereas BTS-logged data exhibit extreme importance weights and very small effective sample sizes (ESS), making IPS-based ranking unreliable under weak support. Switch-DR is most useful in moderate-overlap regimes, where it truncates high-variance corrections. Still, it introduces bias that depends on the switching threshold and must therefore be stress-tested rather than treated as a universally superior estimator. Finally, we provide a structured reporting template—based on oracle decomposition, overlap diagnostics, and estimator components—for explaining why a policy appears better and how reliable that conclusion is.

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