Adapting DeepLabV3+ for biopsy cervical cancer lesion segmentation

IntroductionCervical cancer remains a leading cause of cancer mortality in resource-constrained settings, where access to advanced digital pathology equipment is severely limited. Automated histopathological image segmentation offers a potential pathway to improve diagnostic access, but practical solutions combining affordable hardware with robust deep learning remain underdeveloped.MethodsWe present an approach combining smartphone-assisted microscopy with DeepLabV3+ architecture […]

Optimal Knock-Pick Planning for Tightly Packed Tabletop Blocks With Parallel Grippers

arXiv:2605.17800v1 Announce Type: cross Abstract: Rearranging densely packed tabletop objects is challenging when parallel-gripper picks are infeasible without sufficient clearance around an object. This work studies the problem characteristics for practically motivated settings with uniformly sized blocks placed at planar tabletop grid locations. Since purely prehensile removal can become infeasible, a directional knock primitive is […]

EAGT: Echocardiography Augmentation for Generalisability and Transferability

arXiv:2605.16427v1 Announce Type: cross Abstract: Deep learning models for echocardiography segmentation often struggle to generalise across institutions, scanners, and patient populations, where collecting large, consistently annotated datasets is infeasible. Data augmentation is widely used to improve the robustness of deep learning models; however, its role in enhancing cross-dataset generalisability in echocardiography remains insufficiently understood. This […]

Artificial Intelligence can Recognize Whether a Job Applicant is Selling and/or Lying According to Facial Expressions and Head Movements Much More Correctly Than Human Interviewers

arXiv:2605.17461v1 Announce Type: cross Abstract: Whether an interviewee’s honest and deceptive responses can be detected by facial expression signals in videos has been debated and requires further research. We developed deep learning models enabled by computer vision to extract temporal patterns of job applicants’ facial expressions and head movements to identify self-reported honest and deceptive […]

Learning Relative Representations for Fine-Grained Multimodal Alignment with Limited Data

arXiv:2605.16834v1 Announce Type: cross Abstract: Multimodal pre-training demonstrates strong generalization performance, but this paradigm is often impractical in domains where paired data are scarce. A promising alternative is post-hoc multimodal alignment, which aligns separately pre-trained unimodal encoders using a limited number of paired examples. However, existing methods focus primarily on aligning global representations, missing patch-token […]

How to Instruct Your Robot: Dense Language Annotations Power Robot Policy Learning

arXiv:2605.17077v1 Announce Type: cross Abstract: Scaling robot policy learning is bottlenecked by the cost of collecting demonstrations, while language annotations for existing demonstrations are comparatively cheap. We study language density as a lever for extracting more signal from a fixed robot or egocentric-video corpus. We introduce DeMiAn (Dense Multi-aspect Annotation), a two-stage approach that first […]

Avoiding Structural Failure Modes in Tabular Fair SSL: Online Primal-Dual Allocation under Confidence Gating

arXiv:2605.16446v1 Announce Type: cross Abstract: Semi-supervised learning (SSL) enables prediction with limited labels, but high-stakes tabular applications (medical, credit, recidivism) require statistical fairness guarantees. We identify a structural conflict in tabular fair SSL through a diagnostic stress test: under confidence-gated pseudo-labeling, moment-matching fairness regularizers can trigger two failure modes — Masking Collapse (fairness erodes confidence, […]

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