TY  - GEN
N2  - This book includes high-quality papers presented at the Second International Symposium on Computer Vision and Machine Intelligence in Medical Image Analysis (ISCMM 2021), organized by Computer Applications Department, SMIT in collaboration with Department of Pathology, SMIMS, Sikkim, India, and funded by Indian Council of Medical Research, during 11 12 November 2021. It discusses common research problems and challenges in medical image analysis, such as deep learning methods. It also discusses how these theories can be applied to a broad range of application areas, including lung and chest x-ray, breast CAD, microscopy and pathology. The studies included mainly focus on the detection of events from biomedical signals.
DO  - 10.1007/978-981-19-0151-5
DO  - doi
AB  - This book includes high-quality papers presented at the Second International Symposium on Computer Vision and Machine Intelligence in Medical Image Analysis (ISCMM 2021), organized by Computer Applications Department, SMIT in collaboration with Department of Pathology, SMIMS, Sikkim, India, and funded by Indian Council of Medical Research, during 11 12 November 2021. It discusses common research problems and challenges in medical image analysis, such as deep learning methods. It also discusses how these theories can be applied to a broad range of application areas, including lung and chest x-ray, breast CAD, microscopy and pathology. The studies included mainly focus on the detection of events from biomedical signals.
T1  - Artificial intelligence on medical data :proceedings of International Symposium, ISCMM 2021 /
AU  - Gupta, Mousumi,
AU  - Ghatak, Sujata,
AU  - Gupta, Amlan,
AU  - Mukherjee, Abir Lal,
VL  - volume 37
CN  - RC78.7.D53
N1  - International conference proceedings.
N1  - Includes author index.
ID  - 1452805
KW  - Diagnostic imaging
KW  - Computer vision in medicine
SN  - 9789811901515
SN  - 9811901511
TI  - Artificial intelligence on medical data :proceedings of International Symposium, ISCMM 2021 /
LK  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-19-0151-5
UR  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-19-0151-5
ER  -