Genta Ishikawa
Papers
1
Total Citations
7
H-Index
1
About
Genta Ishikawa is a researcher whose work sits at the intersection of medical imaging and artificial intelligence, with a primary focus on advancing fetal health diagnostics. Their most impactful contribution, a 2019 study on detecting fetuses in ultrasound images, has garnered 7 citations and introduces a novel method for automatically estimating fetal position. By fine-tuning a Convolutional Neural Network (CNN) on ultrasound data and employing Grad-CAM for visual explanations, Ishikawa’s approach enables the classification of specific fetal parts—such as the leg—and locates the fetus within the uterus. This work is notable for bridging the gap between deep learning interpretability and clinical prenatal care, offering a non-invasive, automated tool that could assist sonographers in assessing fetal presentation. Ishikawa’s research underscores a commitment to making AI-driven diagnostics more transparent and practical in real-world medical settings, laying groundwork for safer, more efficient pregnancy monitoring. Their contributions highlight the potential of computer vision to transform routine obstetric examinations.
Research Focus
Key Achievements
Top Papers
- 1