arXiv Machine Learning By Syed Muhammad Ibne Zulfiker, Tanzima Hashem, Fariha Tabassum Islam, Md Sultanul Arifin, Khandker Aftarul Islam, Nishat Anjum Bristy, Faria Huq, Priyeta Saha, Syeda Nahida Akter, Arpita Saha

Mobile Imaging Solutions for Medical Diagnosis: Trends and Applications

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arXiv Machine Learning
Sep 4

Beyond Blur: A Semantic Tri-view Pipeline for Teledermatology Gradability via Skin Micro-relief

The paper introduces the Semantic Tri-view Pipeline, an interpretable system that automatically screens teledermatology photographs for gradability by analyzing epidermal micro-relief across up to three smartphone views. It uses a lightweight DeepLabV3+ model to segment micro-relief fidelity and aggregates the resulting spatial masks with logistic regression, leveraging viewpoint redundancy to improve robustness. Evaluated on the SCIN dataset, the approach raises the AUC from 0.81 to 0.96 on optically clear cases, offering real‑time, privacy‑by‑design feedback to filter ungradable photo sets before clinician review.

By Robert Engel
arXiv Computer Vision
Aug 26

TLNM: Externally Validated Tooth Detection, Numbering and Segmentation from Smartphone Photographs Using Mask R-CNN

The paper presents TLNM, a Mask R‑CNN based system that detects, numbers, and segments teeth in smartphone photographs. It incorporates a masked gray‑world white‑balancing step and an anatomically constrained detection layer to handle patient‑generated variability. Evaluated on internal and external datasets, the model achieved high AP50, PQ, and F1 scores, demonstrating robust performance across diverse populations and imaging conditions.

By Arash Nedaei, Henna Tiensuu, Elina V\"ayrynen, Saujanya Karki, Jaakko Suutala
arXiv Computer Vision
Sep 18

WoundAIssist: Development and Evaluation of an AI-Based Mobile Application for Remote Chronic Wound Care in Elderly Patients

WoundAIssist is an AI‑driven mobile application designed to support remote chronic wound care for elderly patients. It allows patients to capture wound images and complete questionnaires at home, while physicians monitor progress through remote video consultations and on‑device deep‑learning segmentation. A usability study involving patients and dermatologists found the app to be highly usable, of good quality, and positively received for its AI‑driven wound recognition.

By Vanessa Borst, Anna Riedmann, Tassilo Dege, Konstantin M\"uller, Astrid Schmieder, Birgit Lugrin, Samuel Kounev
arXiv Machine Learning
Aug 14

A Cloud-Edge System for Multimodal Clinical Screening in Resource-Constrained Rural Settings

arXiv:2608. 12745v1 Announce Type: new Abstract: Medical AI has demonstrated specialist-level diagnostic accuracy, yet these capabilities remain largely inaccessible in resource-constrained rural settings where bandwidth is scarce, compute is limited, and clinical decision-making requires integrating heterogeneous modalities.

By Hei Ting (Una), Chan, Chenwei Wu, Xueshen Liu, Zesen Zhao, Boyuan Zheng, Luis Filipe Nakayama, Michael G. Morley, Liyue Shen, Jiasi Chen, Z. Morley Mao